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Abstract Lithium-ion batteries (LiBs) are used globally as a key component of clean and sustainable energy infrastructure, and emerging LiB technologies have incorporated a class of per- and polyfluoroalkyl substances (PFAS) known as bis-perfluoroalkyl sulfonimides (bis-FASIs). PFAS are recognized internationally as recalcitrant contaminants, a subset of which are known to be mobile and toxic, but little is known about environmental impacts of bis-FASIs released during LiB manufacture, use, and disposal. Here we demonstrate that environmental concentrations proximal to manufacturers, ecotoxicity, and treatability of bis-FASIs are comparable to PFAS such as perfluorooctanoic acid that are now prohibited and highly regulated worldwide, and we confirm the clean energy sector as an unrecognized and potentially growing source of international PFAS release. Results underscore that environmental impacts of clean energy infrastructure merit scrutiny to ensure that reduced CO2 emissions are not achieved at the expense of increasing global releases of persistent organic pollutants.

Abstract Background Sex differences in mitochondrial function have been reported in multiple tissue and cell types. Additionally, sex-variable responses to stressors including environmental pollutants and drugs that cause mitochondrial toxicity have been observed. The mechanisms that establish these differences are thought to include hormonal modulation, epigenetic regulation, double dosing of X-linked genes, and the maternal inheritance of mtDNA. Understanding the drivers of sex differences in mitochondrial function and being able to model them in vitro is important for identifying toxic compounds with sex-variable effects. Additionally, understanding how sex differences in mitochondrial function compare across species may permit insight into the drivers of these differences, which is important for basic biology research. This study explored whether Caenorhabditis elegans, a model organism commonly used to study stress biology and toxicology, exhibits sex differences in mitochondrial function and toxicant susceptibility. To assess sex differences in mitochondrial function, we utilized four male enriched populations (N2 wild-type male enriched, fog-2(q71), him-5(e1490), and him-8(e1498)). We performed whole worm respirometry and determined whole worm ATP levels and mtDNA copy number. To probe whether sex differences manifest only after stress and inform the growing use of C. elegans as a mitochondrial health and toxicologic model, we also assessed susceptibility to a classic mitochondrial toxicant, rotenone. Results We detected few to no large differences in mitochondrial function between C. elegans sexes. Though we saw no sex differences in vulnerability to rotenone, we did observe sex differences in the uptake of this lipophilic compound, which may be of interest to those utilizing C. elegans as a model organism for toxicologic studies. Additionally, we observed altered non-mitochondrial respiration in two him strains, which may be of interest to other researchers utilizing these strains. Conclusions Basal mitochondrial parameters in male and hermaphrodite C. elegans are similar, at least at the whole-organism level, as is toxicity associated with a mitochondrial Complex I inhibitor, rotenone. Our data highlights the limitation of using C. elegans as a model to study sex-variable mitochondrial function and toxicological responses.

Abstract The rate of soil-transmitted helminth (STH) infection is estimated to be around 20% in Indonesia. Health promotion and health education are cost-effective strategies to supplement STH prevention and control programs. Existing studies suggest that quantitative tools for knowledge, attitudes and practices (KAP) are important to monitor effective community-based STH interventions. However, evidence is limited regarding the applicability of such tools. This study aims to identify the socio-demographic predictors for STH-related knowledge and practices and validate the quantitative tools in population use. A cross-sectional study design was conducted among residents of 16 villages in Central Java, Indonesia. Adult and child respondents were interviewed to assess general knowledge and practices in relation to STH. Two mixed effects models identified the significant factors in predicting knowledge and practice scores. The model predicted knowledge and practice scores were compared with the observed scores to validate the quantitative measurements developed in this study. Participants’ socio-demographic variables were significant in predicting an individual’s STH-related knowledge level and their hand washing and hygiene practices, taking into account household-level variability. Model validation results confirmed that the quantitative measurement tools were suitable for assessing STH associated knowledge and behaviour. The questionnaire developed in this study can be used to support school- and community-based health education interventions to maximize the effect of STH prevention and control programs.

The latest operational National Air Quality Forecast Capability (NAQFC) has been advanced to use the Community Multiscale Air Quality (CMAQ) model (version 5.3.1) with the CB6r3 (Carbon Bond 6 revision 3) AERO7 (version 7 of the aerosol module) chemical mechanism and is driven by the Finite-Volume Cubed-Sphere (FV3) Global Forecast System, version 16 (GFSv16). This update has been accomplished via the development of the meteorological preprocessor, NOAA-EPA Atmosphere–Chemistry Coupler (NACC), adapted from the existing Meteorology–Chemistry Interface Processor (MCIP). Differing from the typically used Weather Research and Forecasting (WRF) CMAQ system in the air quality research community, the interpolation-based NACC can use various meteorological outputs to drive the CMAQ model (e.g., FV3-GFSv16), even though they are on different grids. In this study, we compare and evaluate GFSv16-CMAQ and WRFv4.0.3-CMAQ using observations over the contiguous United States (CONUS) in summer 2019 that have been verified with surface meteorological and AIRNow observations. During this period, the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign was performed, and we compare the two models with airborne measurements from the NASA DC-8 aircraft. The GFS-CMAQ and WRF-CMAQ systems show similar performance overall with some differences for certain events, species and regions. The GFSv16 meteorology tends to have a stronger diurnal variability in the planetary boundary layer height (higher during daytime and lower at night) than WRF over the US Pacific coast, and it also predicted lower nighttime 10 m winds. In summer 2019, the GFS-CMAQ system showed better surface ozone (O3) than WRF-CMAQ at night over the CONUS domain; however, the models' fine particulate matter (PM2.5) predictions showed mixed verification results: GFS-CMAQ yielded better mean biases but poorer correlations over the Pacific coast. These results indicate that using global GFSv16 meteorology with NACC to directly drive CMAQ via interpolation is feasible and yields reasonable results compared to the commonly used WRF approach.

We describe and benchmark a new quantum charge-coupled device (QCCD) trapped-ion quantum computer based on a linear trap with periodic boundary conditions, which resembles a race track. The new system successfully incorporates several technologies crucial to future scalability—including electrode broadcasting, multilayer rf routing, and magneto-optical trap (MOT) loading—while maintaining, and in some cases exceeding, the gate fidelities of previous QCCD systems. The system is initially operated with 32 qubits, but future upgrades will allow for more. We benchmark the performance of primitive operations, including an average state preparation and measurement error of 1.6(1)×10^{-3}, an average single-qubit gate infidelity of 2.5(3)×10^{-5}, and an average two-qubit gate infidelity of 1.84(5)×10^{-3}. The system-level performance of the quantum processor is assessed with mirror benchmarking, linear cross-entropy benchmarking, a quantum volume measurement of QV=2^{16}, and the creation of 32-qubit entanglement in a GHZ state. We also tested application benchmarks, including Hamiltonian simulation, QAOA, error correction on a repetition code, and dynamics simulations using qubit reuse. We also discuss future upgrades to the new system aimed at adding more qubits and capabilities.

Cyclic volatile methyl siloxanes (cVMSs), which can volatilize into the atmosphere, are active ingredients in widely used consumer products, thus attracting much attention due to their potential environmental risks. While in the atmosphere the cVMSs undergo oxidation, yielding both gaseous and particulate products. The aerosol yields and compositions from the OH oxidation of four cVMSs (D3–D6) were determined under low- and high-NOx conditions in an oxidation flow reactor. The aerosol yields increased progressively from D3 to D6, consistent with the volatilities and molecule weights of these cVMSs. NOx can restrict the formation of secondary organic aerosols (SOAs), leading to lower SOA yields under high-NOx conditions than under low-NOx conditions, with a yield decrease between 0.05–0.30 depending on the cVMSs. Ammonium sulfate seeds exhibited minor impacts on SOA yields under low-NOx conditions but significantly increased the SOA yields in the oxidation of D3–D5 at short photochemical ages under high-NOx conditions. The mass spectra of the SOAs showed a dependence of its chemical compositions on OH exposure. At high exposures equivalent to photochemical ages of >4 d in the atmosphere, D4–D6 SOAs mainly consisted of CxHy and CxHyOzSin under low-NOx conditions, whereas they primarily contained NmOz, CxHy, CxHyO1, CxHyO>1 and CxHyOzSin under high-NOx conditions. The potential contributions of cVMSs to SOA formation in the atmosphere were evaluated using the reported cVMSs annual production and the yield obtained in the present study. A global cVMS-derived (D4–D6) SOA source strength is estimated to be 0.01 Tg yr−1, distributed over major urban centers.

A new dynamical core, known as the Finite-Volume Cubed-Sphere (FV3) and developed at both NASA and NOAA, is used in NOAA's Global Forecast System (GFS) and in limited-area models for regional weather and air quality applications. NOAA has also upgraded the operational FV3GFS to version 16 (GFSv16), which includes a number of significant developmental advances to the model configuration, data assimilation, and underlying model physics, particularly for atmospheric composition to weather feedback. Concurrent with the GFSv16 upgrade, we couple the GFSv16 with the Community Multiscale Air Quality (CMAQ) model to form an advanced version of the National Air Quality Forecasting Capability (NAQFC) that will continue to protect human and ecosystem health in the US. Here we describe the development of the FV3GFSv16 coupling with a “state-of-the-science” CMAQ model version 5.3.1. The GFS–CMAQ coupling is made possible by the seminal version of the NOAA-EPA Atmosphere–Chemistry Coupler (NACC), which became a major piece of the next operational NAQFC system (i.e., NACC-CMAQ) on 20 July 2021. NACC-CMAQ has a number of scientific advancements that include satellite-based data acquisition technology to improve land cover and soil characteristics and inline wildfire smoke and dust predictions that are vital to predictions of fine particulate matter (PM2.5) concentrations during hazardous events affecting society, ecosystems, and human health. The GFS-driven NACC-CMAQ model has significantly different meteorological and chemical predictions compared to the previous operational NAQFC, where evaluation of NACC-CMAQ shows generally improved near-surface ozone and PM2.5 predictions and diurnal patterns, both of which are extended to a 72 h (3 d) forecast with this system.

Although air quality in the United States has improved remarkably in the past decades, ground-level ozone (O3) often rises in exceedance of the national ambient air quality standard in nonattainment areas, including the Long Island Sound (LIS) and its surrounding areas. Accurate prediction of high-ozone episodes is needed to assist government agencies and the public in mitigating harmful effects of air pollution. In this study, we have developed a suite of potential forecast improvements, including dynamic boundary conditions, rapid emission refresh and chemical data assimilation, in a 3 km resolution Community Multiscale Air Quality (CMAQ) modeling system. The purpose is to evaluate and assess the effectiveness of these forecasting techniques, individually or in combination, in improving forecast guidance for two major air pollutants: surface O3 and nitrogen dioxide (NO2). Experiments were conducted for a high-O3 episode (28–29 August 2018) during the Long Island Sound Tropospheric Ozone Study (LISTOS) field campaign, which provides abundant observations for evaluating model performance. The results show that these forecast system updates are useful in enhancing the capability of this 3 km forecasting model with varying effectiveness for different pollutants. For O3 prediction, the most significant improvement comes from the dynamic boundary conditions derived from the NOAA operational forecast system, National Air Quality Forecast Capability (NAQFC), which increases the correlation coefficient (R) from 0.81 to 0.93 and reduces the root mean square error (RMSE) from 14.97 to 8.22 ppbv, compared to that with the static boundary conditions (BCs). The NO2 from all high-resolution simulations outperforms that from the operational 12 km NAQFC simulation, regardless of the BCs used, highlighting the importance of spatially resolved emission and meteorology inputs for the prediction of short-lived pollutants. The effectiveness of improved initial concentrations through optimal interpolation (OI) is shown to be high in urban areas with high emission density. The influence of OI adjustment, however, is maintained for a longer period in rural areas, where emissions and chemical transformation make a smaller contribution to the O3 budget than that in high-emission areas. Following the assessment of individual updates, the forecasting system is configured with dynamic boundary conditions, optimal interpolation of initial concentrations and emission adjustment, to simulate a high-ozone episode during the 2018 LISTOS field campaign. The newly developed forecasting system significantly reduces the bias of surface NO2 prediction. When compared with the NASA Langley GeoCAPE Airborne Simulator (GCAS) vertical column density (VCD), this system is able to reproduce the NO2 VCD with a higher correlation (0.74), lower normalized mean bias (40 %) and normalized mean error (61 %) than NAQFC (0.57, 45 % and 76 %, respectively). The 3 km system captures magnitude and timing of surface O3 peaks and valleys better. In comparison with lidar, O3 profile variability of the vertical O3 is captured better by the new system (correlation coefficient of 0.71) than by NAQFC (correlation coefficient of 0.54). Although the experiments are limited to one pollution episode over the Long Island Sound, this study demonstrates feasible approaches to improve the predictability of high-O3 episodes in contemporary urban environments.

Studies have identified hundreds of per- and polyfluoroalkyl substances (PFAS) in aqueous film forming foam (AFFF) using high resolution mass spectrometry (HRMS), and there is increasing reliance on HRMS with suspect screening in areas such as PFAS fate and transport. Characterization of active-use AFFF formulations is crucial for maintaining representative lists of PFAS for such efforts. Herein, targeted analysis, total oxidizable precursor assay (TOP), suspect screening, and non-targeted analysis were used to characterize PFAS in an AFFF currently certified for use by the U.S. military. The sum of PFAS identified during targeted analysis and suspect screening was compared to TOP, demonstrating that > 90% (20.2 mM) of the estimated total PFAS concentration (22.4 mM) was comprised of ''unknown'' PFAS. Tandem and multi-stage tandem mass spectra were used to annotate 10 PFAS within 9 classes, 8 of which have never been reported. A subset are fluorotelomer-based isomers of legacy, electrochemically synthesized PFAS. Thus, suspect screening efforts that rely solely on accurate mass matching may mis-annotate PFAS presented here as isomers that will have key differences in properties such as biotransformation pathways. The total estimated concentration of the 10 PFAS was ~20 mM, which agrees with the ''unknown'' fraction (20.2 mM) identified by TOP.

As a candidate for the next-generation National Air Quality Forecast Capability (NAQFC), the meteorological forecast from the Global Forecast System with the new Finite Volume Cube-Sphere dynamical core (GFS–FV3) will be applied to drive the chemical evolution of gases and particles described by the Community Multiscale Air Quality modeling system. CMAQv5.0.2, a historical version of CMAQ, has been coupled with the North American Mesoscale Forecast System (NAM) model in the current operational NAQFC. An experimental version of the NAQFC based on the offline-coupled GFS–FV3 version 15 with CMAQv5.0.2 modeling system (GFSv15–CMAQv5.0.2) has been developed by the National Oceanic and Atmospheric Administration (NOAA) to provide real-time air quality forecasts over the contiguous United States (CONUS) since 2018. In this work, comprehensive region-specific, time-specific, and categorical evaluations are conducted for meteorological and chemical forecasts from the offline-coupled GFSv15–CMAQv5.0.2 for the year 2019. The forecast system shows good overall performance in forecasting meteorological variables with the annual mean biases of −0.2 ∘C for temperature at 2 m, 0.4 % for relative humidity at 2 m, and 0.4 m s−1 for wind speed at 10 m compared to the METeorological Aerodrome Reports (METAR) dataset. Larger biases occur in seasonal and monthly mean forecasts, particularly in spring. Although the monthly accumulated precipitation forecasts show generally consistent spatial distributions with those from the remote-sensing and ensemble datasets, moderate-to-large biases exist in hourly precipitation forecasts compared to the Clean Air Status and Trends Network (CASTNET) and METAR. While the forecast system performs well in forecasting ozone (O3) throughout the year and fine particles with a diameter of 2.5 µm or less (PM2.5) for warm months (May–September), it significantly overpredicts annual mean concentrations of PM2.5. This is due mainly to the high predicted concentrations of fine fugitive and coarse-mode particle components. Underpredictions in the southeastern US and California during summer are attributed to missing sources and mechanisms of secondary organic aerosol formation from biogenic volatile organic compounds (VOCs) and semivolatile or intermediate-volatility organic compounds. This work demonstrates the ability of FV3-based GFS in driving the air quality forecasting. It identifies possible underlying causes for systematic region- and time-specific model biases, which will provide a scientific basis for further development of the next-generation NAQFC.

The National Air Quality Forecast Capability (NAQFC) operated in the US National Oceanic and Atmospheric Administration (NOAA) provides the operational forecast guidance for ozone and fine particulate matter with aerodynamic diameters less than 2.5 µm (PM2.5) over the contiguous 48 US states (CONUS) using the Community Multi-scale Air Quality (CMAQ) model. The existing NAQFC uses climatological chemical lateral boundary conditions (CLBCs), which cannot capture pollutant intrusion events originating outside of the model domain. In this study, we developed a model framework to use dynamic CLBCs from the Goddard Earth Observing System Model, version 5 (GEOS) to drive NAQFC. A mapping of the GEOS chemical species to CMAQ's CB05–AERO6 (Carbon Bond 5; version 6 of the aerosol module) species was developed. The utilization of the GEOS dynamic CLBCs in NAQFC showed the best overall performance in simulating the surface observations during the Saharan dust intrusion and Canadian wildfire events in summer 2015. The simulated PM2.5 was improved from 0.18 to 0.37, and the mean bias was reduced from −6.74 to −2.96 µg m−3 over CONUS. Although the effect of CLBCs on the PM2.5 correlation was mainly near the inflow boundary, its impact on the background concentrations reached further inside the domain. The CLBCs could affect background ozone concentrations through the inflows of ozone itself and its precursors, such as CO. It was further found that the aerosol optical thickness (AOT) from satellite retrievals correlated well with the column CO and elemental carbon from GEOS. The satellite-derived AOT CLBCs generally improved the model performance for the wildfire intrusion events during a summer 2018 case study and demonstrated how satellite observations of atmospheric composition could be used as an alternative method to capture the air quality effects of intrusions when the CLBCs of global models, such as GEOS CLBCs, are not available.

Forest fires are major contributors of reactive gas- and particle-phase organic compounds to the atmosphere. We used offline high-resolution tandem mass spectrometry to perform a molecular-level speciation of gas- and particle-phase compounds sampled via aircraft from an evolving boreal forest fire smoke plume in Saskatchewan, Canada. We observed diverse multifunctional compounds containing oxygen, nitrogen, and sulfur (CHONS), whose structures, formation, and impacts are understudied. The dilution-corrected absolute ion abundance of particle-phase CHONS compounds increased with plume age by a factor of 6.4 over the first 4 h of downwind transport, and their relative contribution to the observed functionalized organic aerosol (OA) mixture increased from 19 % to 40 %. The dilution-corrected absolute ion abundance of particle-phase compounds with sulfide functional groups increased by a factor of 13 with plume age, and their relative contribution to observed OA increased from 4 % to 40 %. Sulfides were present in up to 75 % of CHONS compounds and the increases in sulfides were accompanied by increases in ring-bound nitrogen; both increased together with CHONS prevalence. A complex mixture of intermediate- and semi-volatile gas-phase organic sulfur species was observed in emissions from the fire and depleted downwind, representing potential precursors to particle-phase CHONS compounds. These results demonstrate CHONS formation from nitrogen- and oxygen-containing biomass burning emissions in the presence of reduced sulfur species. In addition, they highlight chemical pathways that may also be relevant in situations with elevated emissions of nitrogen- and sulfur-containing organic compounds from residential biomass burning and fossil fuel use (e.g., coal), respectively.

Multiple observation data sets – Interagency Monitoring of Protected Visual Environments (IMPROVE) network data, the Automated Smoke Detection and Tracking Algorithm (ASDTA), Hazard Mapping System (HMS) smoke plume shapefiles and aircraft acetonitrile (CH3CN) measurements from the NOAA Southeast Nexus (SENEX) field campaign – are used to evaluate the HMS–BlueSky–SMOKE (Sparse Matrix Operator Kernel Emission)–CMAQ (Community Multi-scale Air Quality Model) fire emissions and smoke plume prediction system. A similar configuration is used in the US National Air Quality Forecasting Capability (NAQFC). The system was found to capture most of the observed fire signals. Usage of HMS-detected fire hotspots and smoke plume information was valuable for deriving both fire emissions and forecast evaluation. This study also identified that the operational NAQFC did not include fire contributions through lateral boundary conditions, resulting in significant simulation uncertainties. In this study we focused both on system evaluation and evaluation methods. We discussed how to use observational data correctly to retrieve fire signals and synergistically use multiple data sets. We also addressed the limitations of each of the observation data sets and evaluation methods.

Oil-sands (OS) operations in Alberta, Canada, are a large source of secondary organic aerosol (SOA). However, the SOA formation process from OS-related precursors remains poorly understood. In this work, a newly developed oxidation flow reactor (OFR), the Environment and Climate Change Canada OFR (ECCC-OFR), was characterized and used to study the yields and composition of SOA formed from OH oxidation of α-pinene, selected alkanes, and the vapors evolved from five OS-related samples (OS ore, naphtha, tailings pond water, bitumen, and dilbit). The derived SOA yields from α-pinene and selected alkanes using the ECCC-OFR were in good agreement with those of traditional smog chamber experiments but significantly higher than those of other OFR studies under similar conditions. The results also suggest that gas-phase reactions leading to fragmentation (i.e., C–C bond cleavage) have a relatively small impact on the SOA yields in the ECCC-OFR at high photochemical ages, in contrast to other previously reported OFR results. Translating the impact of fragmentation reactions in the ECCC-OFR to ambient atmospheric conditions reduces its impact on SOA formation even further. These results highlight the importance of careful evaluation of OFR data, particularly when using such data to provide empirical factors for the fragmentation process in models. Application of the ECCC-OFR to OS-related precursor mixtures demonstrated that the SOA yields from OS ore and bitumen vapors (maximum of ∼0.6–0.7) are significantly higher than those from the vapors from solvent use (naphtha), effluent from OS processing (tailings pond water), and from the solvent diluted bitumen (dilbit; maximum of ∼0.2–0.3), likely due to the volatility of each precursor mixture. A comparison of the yields and elemental ratios (H∕C and O∕C) of the SOA from the OS-related precursors to those of linear and cyclic alkane precursors of similar carbon numbers suggests that cyclic alkanes play an important role in the SOA formation in the OS. The analysis further indicates that the majority of the SOA formed downwind of OS facilities is derived from open-pit mining operations (i.e., OS ore evaporative emissions) rather than from higher-volatility precursors from solvent use during processing and/or tailings management. The current results have implications for improving the regional modeling of SOA from OS sources, for the potential mitigation of OS precursor emissions responsible for observed SOA downwind of OS operations, and for the understanding of petrochemical- and alkane-derived SOA in general.

Chronic kidney disease of unknown etiology (CKDu) is an emerging global concern affecting several agricultural communities in the Americas and South Asia. Environmental contaminants such as heavy metals (e.g., Cd, As, Pb, and V) and organic pesticides (e.g., glyphosate) in the drinking water have been hypothesized to play a role in childhood onset and progression of this disease. However, a comprehensive analysis of chemical contaminants in the drinking water and effects of these compounds and their mixtures on kidney development and function remains unknown. Here, we conducted targeted and non-targeted chemical analyses of sediment and drinking water in CKDu affected regions in Sri Lanka, one of the most affected countries. Using zebrafish Danio rerio, a toxicology and kidney disease model, we then examined kidney developmental effects of exposure to (i) environmentally derived samples from CKDu endemic and non-endemic regions and (ii) Cd, As, V, Pb, and glyphosate as individual compounds and in mixtures. We found that drinking water is contaminated with various organic chemicals including nephrotoxic compounds as well as heavy metals, but at levels considered safe for drinking. Histological studies and gene expression analyses examining markers of kidney development (pax2a) and kidney injury (kim1) showed novel metal and glyphosate-metal mixture specific effects on kidney development. Mitochondrial dysfunction is directly linked to kidney failure, and examination of mixture specific mitochondrial toxicity showed altered mitochondrial function following treatment with environmental samples from endemic regions. Collectively, we show that metals in drinking water, even at safe levels, can impede kidney development at an early age, potentiating increased susceptibility to other agrochemicals such as glyphosate. Drinking water contaminant effects on mitochondria can further contribute to progression of kidney dysfunction and our mitochondrial assay may help identify regions at risk of CKDu.

The NEMS GFS Aerosol Component Version 2.0 (NGACv2) for global multispecies aerosol forecast has been developed at the National Centers of Environment Prediction (NCEP) in collaboration with the NESDIS Center for Satellite Applications and Research (STAR), the NASA Goddard Space Flight Center (GSFC), and the University at Albany, State University of New York (SUNYA). This paper describes the continuous development of the NGAC system at NCEP after the initial global dust-only forecast implementation (NGAC version 1.0, NGACv1). With NGACv2, additional sea salt, sulfate, organic carbon, and black carbon aerosol species were included. The smoke emissions are from the NESDIS STAR's Global Biomass Burning Product (GBBEPx), blended from the global biomass burning emission product from a constellation of geostationary satellites (GBBEP-Geo) and GSFC's Quick Fire Emission Data Version 2 from a polar-orbiting sensor (QFED2). This implementation advanced the global aerosol forecast capability and made a step forward toward developing a global aerosol data assimilation system. The aerosol products from this system have been used by many applications such as for regional air quality model lateral boundary conditions, satellite sea surface temperature (SST) physical retrievals, and the global solar insolation estimation. Positive impacts have been seen in these applications.

This study applies the Gridpoint Statistical Interpolation (GSI) 3D-Var assimilation tool originally developed by the National Centers for Environmental Prediction (NCEP), to improve surface PM2.5 predictions over the contiguous United States (CONUS) by assimilating aerosol optical depth (AOD) and surface PM2.5 in version 5.1 of the Community Multi-scale Air Quality (CMAQ) modeling system. An optimal interpolation (OI) method implemented earlier (Tang et al., 2015) for the CMAQ modeling system is also tested for the same period (July 2011) over the same CONUS. Both GSI and OI methods assimilate surface PM2.5 observations at 00:00, 06:00, 12:00 and 18:00 UTC, and MODIS AOD at 18:00 UTC. The assimilations of observations using both GSI and OI generally help reduce the prediction biases and improve correlation between model predictions and observations. In the GSI experiments, assimilation of surface PM2.5 (particle matter with diameter 2.5 compared to its MODIS AOD assimilation at the 550 nm wavelength. In contrast, we find a stronger OI impact of the MODIS AOD on surface aerosols at 18:00 UTC compared to the surface PM2.5 OI method. GSI produces smoother result and yields overall better correlation coefficient and root mean squared error (RMSE). It should be noted that the 3D-Var and OI methods used here have several big differences besides the data assimilation schemes. For instance, the OI uses relatively big model uncertainties, which helps yield smaller mean biases, but sometimes causes the RMSE to increase. We also examine and discuss the sensitivity of the assimilation experiments' results to the AOD forward operators.

It is a well-known fact that a cluster of nucleons can be formed in the interior of an atomic nucleus, and such clusters may occupy molecular-like orbitals, showing characteristics similar to normal molecules consisting of atoms. Chemical molecules having a linear alignment are commonly seen in nature, such as carbon dioxide. A similar linear alignment of the nuclear clusters, referred to as linear-chain cluster state (LCCS), has been studied since the 1950s, however, up to now there is no clear experimental evidence demonstrating the existence of such a state. Recently, it was proposed that an excess of neutrons may offer just such a stabilizing mechanism, revitalizing interest in the nuclear LCCS, specifically with predictions for their emergence in neutron-rich carbon isotopes. Here we present the experimental observation of α-cluster states in the radioactive 14C nucleus. Using the Be10+α resonant scattering method with a radioactive beam, we observed a series of levels which completely agree with theoretically predicted levels having an explicit linear-chain cluster configuration. We regard this as the first strong indication of the linear-chain clustered nucleus.

Land and atmospheric initial conditions of the Weather Research and Forecasting (WRF) model are often interpolated from a different model output. We perform case studies during NASA's SEAC4RS and DISCOVER-AQ Houston airborne campaigns, demonstrating that using land initial conditions directly downscaled from a coarser resolution dataset led to significant positive biases in the coupled NASA-Unified WRF (NUWRF, version 7) surface and near-surface air temperature and planetary boundary layer height (PBLH) around the Missouri Ozarks and Houston, Texas, as well as poorly partitioned latent and sensible heat fluxes. Replacing land initial conditions with the output from a long-term offline Land Information System (LIS) simulation can effectively reduce the positive biases in NUWRF surface air temperature by ∼ 2 °C. We also show that the LIS land initialization can modify surface air temperature errors almost 10 times as effectively as applying a different atmospheric initialization method. The LIS-NUWRF-based isoprene emission calculations by the Model of Emissions of Gases and Aerosols from Nature (MEGAN, version 2.1) are at least 20 % lower than those computed using the coarser resolution data-initialized NUWRF run, and are closer to aircraft-observation-derived emissions. Higher resolution MEGAN calculations are prone to amplified discrepancies with aircraft-observation-derived emissions on small scales. This is possibly a result of some limitations of MEGAN's parameterization and uncertainty in its inputs on small scales, as well as the representation error and the neglect of horizontal transport in deriving emissions from aircraft data. This study emphasizes the importance of proper land initialization to the coupled atmospheric weather modeling and the follow-on emission modeling. We anticipate it to also be critical to accurately representing other processes included in air quality modeling and chemical data assimilation. Having more confidence in the weather inputs is also beneficial for determining and quantifying the other sources of uncertainties (e.g., parameterization, other input data) of the models that they drive.

A detailed study of the structure of the doubly mid-shell nucleus Dy104166170 has been carried out, following isomeric and β decay. We have measured the yrast band up to the spin-parity Jπ=6+ state, the K=2 γ-vibration band up to the 5+ state, a low-lying negative-parity band based on a 2− state that could be a candidate for the lowest energy octupole vibration state within this nucleus, and a candidate for the Kπ=6+ two quasi-particle isomer. This state was determined to have an excitation energy of 1643.91(23) keV and a half life of 0.99(4) μs, with a reduced hindrance for its decay to the ground-state band an order of magnitude lower than predicted by NpNn systematics. This is interpreted as being due to γ-vibrational mixing from a near degeneracy of the isomer and the 6+ state of the γ band. Furthermore, the parent nucleus 170Tb has been determined to have a half-life of 0.91(−13+18) s with a possible spin-parity of 2−. Keywords: K isomer, Dysprosium, Mid-shell, γ-Ray spectroscopy

Nitrogen dioxide vertical column density (NO2 VCD) measurements via satellite are compared with a fine-scale regional chemistry transport model, using a new approach that considers varying satellite footprint sizes. Space-borne NO2 VCD measurement has been used as a proxy for surface nitrogen oxide (NOx) emission, especially for anthropogenic urban emission, so accurate comparison of satellite and modeled NO2 VCD is important in determining the future direction of NOx emission policy. The NASA Ozone Monitoring Instrument (OMI) NO2 VCD measurements, retrieved by the Royal Netherlands Meteorological Institute (KNMI), are compared with a 12 km Community Multi-scale Air Quality (CMAQ) simulation from the National Oceanic and Atmospheric Administration. We found that the OMI footprint-pixel sizes are too coarse to resolve urban NO2 plumes, resulting in a possible underestimation in the urban core and overestimation outside. In order to quantify this effect of resolution geometry, we have made two estimates. First, we constructed pseudo-OMI data using fine-scale outputs of the model simulation. Assuming the fine-scale model output is a true measurement, we then collected real OMI footprint coverages and performed conservative spatial regridding to generate a set of fake OMI pixels out of fine-scale model outputs. When compared to the original data, the pseudo-OMI data clearly showed smoothed signals over urban locations, resulting in roughly 20–30 % underestimation over major cities. Second, we further conducted conservative downscaling of OMI NO2 VCDs using spatial information from the fine-scale model to adjust the spatial distribution, and also applied averaging kernel (AK) information to adjust the vertical structure. Four-way comparisons were conducted between OMI with and without downscaling and CMAQ with and without AK information. Results show that OMI and CMAQ NO2 VCDs show the best agreement when both downscaling and AK methods are applied, with the correlation coefficient R = 0.89. This study suggests that satellite footprint sizes might have a considerable effect on the measurement of fine-scale urban NO2 plumes. The impact of satellite footprint resolution should be considered when using satellite observations in emission policy making, and the new downscaling approach can provide a reference uncertainty for the use of satellite NO2 measurements over most cities.

From plasma-wakefield acceleration as a physics experiment toward a plasma-based accelerator as a user facility, the beam physics issues remaining to be solved are still numerous. Providing beams with high energy, charge, and quality simultaneously, not only within the plasma but also at the user doorstep itself, is the main concern. Despite its tremendous efficiency in particle acceleration, the wakefield displays a complex 3D profile which, associated to the beam-loading field induced by the accelerated beam itself, makes the acceleration of high charge to high energy often incompatible with high beam quality. Beam extraction from the plasma without quality degradation for a transfer either to the next plasma stage or to the user application is another difficulty to consider. This article presents the substantial studies carried out and the different innovative methods employed for tackling all these different issues. Efforts focused on achieving the challenging beam parameters targeted by the EuPRAXIA accelerator facility project. The lessons learned at the end of these in-depth simulations and optimizations are highlighted. The sensitivity to different error sources is also estimated to point out the critical components of such an accelerator. Finally, the needs in terms of laser and plasma parameters are provided.

Dust aerosols affect human life, ecosystems, atmospheric chemistry and climate in various aspects. Some studies have revealed intensified dust activity in the western US during the past decades despite the weaker dust activity in non-US regions. It is important to extend the historical dust records, to better understand their temporal changes, and to use such information to improve the daily dust forecasting skill as well as the projection of future dust activity under the changing climate. This study develops dust records in Arizona in 2005–2013 using multiple observation data sets, including in situ measurements at the surface Air Quality System (AQS) and Interagency Monitoring of Protected Visual Environments (IMPROVE) sites, and level 2 deep blue aerosol product by the Moderate Resolution Imaging Spectroradiometer. The diurnal and inter-annual variability of identified dust events are shown related to observed weather patterns (e.g., wind and soil moisture) and surface conditions (e.g., land cover type and vegetation conditions), suggesting a potential for use of satellite soil moisture and land products to help interpret and predict dust activity. Backtrajectories computed using NOAA's Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model indicate that the Sonoran and Chihuahuan deserts are important dust source regions during identified dust events in Phoenix, Arizona. Finally, we assess the impact of a recent strong dust event on western US air quality, using various observational and modeling data sets, during a period with a stratospheric ozone intrusion event. The capability of the current US National Air Quality Forecasting Capability (NAQFC) Community Multi-scale Air Quality (CMAQ) modeling system to represent the magnitude and the temporal variability of aerosol concentrations is evaluated for this event. Directions for integrating observations to further improve dust emission modeling in CMAQ are also suggested.

A regional air-quality forecast system's model of surface ozone variability based on cloud coverage is evaluated using satellite-observed cloud fraction (CF) information and a surface air-quality monitoring system. We compared CF and daily maximum ozone from the National Oceanic and Atmospheric Administration's National Air Quality Forecast Capability (NOAA NAQFC) with CFs from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the US Environmental Protection Agency's AirNow surface ozone measurements during May to October 2014. We found that observed surface ozone shows a negative correlation with the MODIS CFs, showing around 1 ppb decrease for 10 % MODIS CF change over the contiguous United States, while the correlation of modeled surface ozone with the model CFs is much weaker, showing only −0.5 ppb per 10 % NAQFC CF change. Further, daytime CF differences between MODIS and NAQFC are correlated with modeled surface-ozone biases between AirNow and NAQFC, showing −1.05 ppb per 10 % CF change, implying that spatial and temporal misplacement of the modeled cloud field might have biased modeled surface ozone level. Current NAQFC cloud fields seem to have fewer CFs compared to MODIS cloud fields (mean NAQFC CF = 0.38 and mean MODIS CF = 0.55), contributing up to 35 % of surface-ozone bias in the current NAQFC system.

Previous studies indicate that exposure of fish to pristine single-walled carbon nanotubes (SWCNTs) by oral gavage, causes no overt toxicity, and no appreciable absorption has been observed. However, in the environment, SWCNTs are likely to be present in dietary sources, which may result in differential impacts on uptake and biological effects. Additionally, the potential of these materials to sorb nutrients (proteins, carbohydrates, and lipids) while present in the gastrointestinal (GI) tract may lead to nutrient depletion conditions that impact processes such as growth and reproduction. To test this phenomenon, fathead minnows were fed a commercial diet either with or without SWCNTs for 96 h. Tracking and quantification of SWCNTs using near-infrared fluorescence (NIRF) imaging during feeding studies showed the presence of food does not facilitate transport of SWCNTs across the intestinal epithelia. Targeting genes shown to be responsive to nutrient depletion (peptide transporters, peptide hormones, and lipases) indicated that pept2, a peptide transporter, and cck, a peptide hormone, showed differential mRNA expression by 96 h, a response that may be indicative of nutrient limitation. The results of the current study increase our understanding of the movement of SWCNTs through the GI tract, while the changes in nutrient processing genes highlight a novel mechanism of sublethal toxicity in aquatic organisms.

The National Air Quality Forecast Capability (NAQFC) project provides the US with operational and experimental real-time ozone predictions using two different versions of the three-dimensional Community Multi-scale Air Quality (CMAQ) modeling system. Routine evaluation using near-real-time AIRNow ozone measurements through 2011 showed better performance of the operational ozone predictions. In this work, quality-controlled and -assured Air Quality System (AQS) ozone and nitrogen dioxide (NO2) observations are used to evaluate the experimental predictions in 2010. It is found that both ozone and NO2 are overestimated over the contiguous US (CONUS), with annual biases of +5.6 and +5.1 ppbv, respectively. The annual root mean square errors (RMSEs) are 15.4 ppbv for ozone and 13.4 ppbv for NO2. For both species the overpredictions are most pronounced in the summer. The locations of the AQS monitoring sites are also utilized to stratify comparisons by the degree of urbanization. Comparisons for six predefined US regions show the highest annual biases for ozone predictions in Southeast (+10.5 ppbv) and for NO2 in the Lower Middle (+8.1 ppbv) and Pacific Coast (+7.1 ppbv) regions. The spatial distributions of the NO2 biases in August show distinctively high values in the Los Angeles, Houston, and New Orleans areas. In addition to the standard statistics metrics, daily maximum eight-hour ozone categorical statistics are calculated using the current US ambient air quality standard (75 ppbv) and another lower threshold (70 ppbv). Using the 75 ppbv standard, the hit rate and proportion of correct over CONUS for the entire year are 0.64 and 0.96, respectively. Summertime biases show distinctive weekly patterns for ozone and NO2. Diurnal comparisons show that ozone overestimation is most severe in the morning, from 07:00 to 10:00 local time. For NO2, the morning predictions agree with the AQS observations reasonably well, but nighttime concentrations are overpredicted by around 100%.

To show how remote-sensing products can be used to classify the entire CONUS domain into "geographical regions" and "chemical regimes", we analyzed the results of simulation from the Community Multiscale Air Quality (CMAQ) model version 4.7.1 over the Conterminous United States (CONUS) for August 2009. In addition, we observe how these classifications capture the weekly cycles of ground-level nitrogen oxide (NOx) and ozone (O3) at US EPA Air Quality System (AQS) sites. We use the Advanced Very High Resolution Radiometer (AVHRR) land use dominant categories and the Global Ozone Monitoring Experiment-2 (GOME-2) HCHO/NO2 column density ratios to allocate geographical regions (i.e., "urban", "forest", and "other" regions) and chemical regimes (i.e., "NOx-saturated", "NOx-sensitive", and "mixed" regimes). We also show that CMAQ simulations using GOME-2 satellite-adjusted NOx emissions mitigate the discrepancy between the weekly cycles of NOx from AQS observations and that from CMAQ simulation results. We found geographical regions and chemical regimes do not show a one-to-one correspondence: the averaged HCHO / NO2 ratios for AVHRR "urban" and "forest" regions are 2.1 and 4.0, which correspond to GOME-2 "mixed" and "NOx-sensitive" regimes, respectively. Both AQS-observed and CMAQ-simulated weekly cycles of NOx show high concentrations on weekdays and low concentrations on weekends, but with one- or two-day shifts of weekly high peaks in the simulated results, which eventually introduces the shifts in simulated weekly-low O3 concentration. In addition, whereas the high weekend O3 anomaly is clearly observable at sites over the GOME-2 NOx-saturated regime in both AQS and CMAQ, the weekend effect is not captured at sites over the AVHRR urban region because of the chemical characteristics of the urban sites (≈GOME-2 mixed regime). In addition, the weekend effect from AQS is more clearly discernible at sites above the GOME-2 NOx-saturated regime than at other sites above the CMAQ NOx-saturated regime, suggesting that the GOME-2-based chemical regime classification is more accurate than CMAQ-based chemical classification. Furthermore, the CMAQ simulations using the GOME-2-derived NOx emissions adjustment (decreasing from 462 Gg N to 426 Gg N over the US for August 2009) show large reductions of simulated NOx concentrations (particularly over the urban, or NOx-saturated, regime), and mitigates the large discrepancies between the absolute amount and the weekly pattern of NOx concentrations of the EPA AQS and those of the baseline CMAQ.

This study introduces an observation-based dust identification approach and applies it to reconstruct long-term dust climatology in the western United States. Long-term dust climatology is important for quantifying the effects of atmospheric aerosols on regional and global climate. Although many routine aerosol monitoring networks exist, it is often difficult to obtain dust records from these networks, because these monitors are either deployed far away from dust active regions (most likely collocated with dense population) or contaminated by anthropogenic sources and other natural sources, such as wildfires and vegetation detritus. Here we propose an approach to identify local dust events relying solely on aerosol mass and composition from general-purpose aerosol measurements. Through analyzing the chemical and physical characteristics of aerosol observations during satellite-detected dust episodes, we select five indicators to be used to identify local dust records: (1) high PM10 concentrations; (2) low PM2.5/PM10 ratio; (3) higher concentrations and percentage of crustal elements; (4) lower percentage of anthropogenic pollutants; and (5) low enrichment factors of anthropogenic elements. After establishing these identification criteria, we conduct hierarchical cluster analysis for all validated aerosol measurement data over 68 IMPROVE sites in the western United States. A total of 182 local dust events were identified over 30 of the 68 locations from 2000 to 2007. These locations are either close to the four US Deserts, namely the Great Basin Desert, the Mojave Desert, the Sonoran Desert, and the Chihuahuan Desert, or in the high wind power region (Colorado). During the eight-year study period, the total number of dust events displays an interesting four-year activity cycle (one in 2000–2003 and the other in 2004–2007). The years of 2003, 2002 and 2007 are the three most active dust periods, with 46, 31 and 24 recorded dust events, respectively, while the years of 2000, 2004 and 2005 are the calmest periods, all with single digit dust records. Among these deserts, the Chihuahuan Desert (59 cases) and the Sonoran Desert (62 cases) are by far the most active source regions. In general, the Chihuahuan Desert dominates dust activities in the first half of the eight-year period while the Sonoran Desert in the second half. The monthly frequency of dust events shows a peak from March to July and a second peak in autumn from September to November. The large quantity of dust events occurring in summertime also suggests the prevailing impact of windblown dust across the year. This seasonal variation is consistent with previous model simulations over the United States.

Simulations of ionization-induced injection in a laser driven plasma wakefield show that high-quality electron injectors in the 50–200 MeV range can be achieved in a gas cell with a tailored density profile. Using the PIC code Warp with parameters close to existing experimental conditions, we show that the concentration of N_{2} in a hydrogen plasma with a tailored density profile is an efficient parameter to tune electron beam properties through the control of the interplay between beam loading effects and varying accelerating field in the density profile. For a given laser plasma configuration, with moderate normalized laser amplitude, a_{0}=1.6 and maximum electron plasma density, n_{e0}=4×10^{18} cm^{-3}, the optimum concentration results in a robust configuration to generate electrons at 150 MeV with a rms energy spread of 4% and a spectral charge density of 1.8 pC/MeV.

The dynamics of electron acceleration driven by laser wakefield is studied in detail using the particle-in-cell code WARP with the objective to generate high-quality electron bunches with narrow energy spread and small emittance, relevant for the electron injector of a multistage accelerator. Simulation results, using experimentally achievable parameters, show that electron bunches with an energy spread of ∼11% can be obtained by using an ionization-induced injection mechanism in a mm-scale length plasma. By controlling the focusing of a moderate laser power and tailoring the longitudinal plasma density profile, the electron injection beginning and end positions can be adjusted, while the electron energy can be finely tuned in the last acceleration section.

Abstract Background The increasing incidence of fatal opioid overdose is a public health crisis in Canada. Given growing consensus that this crisis is related to the presence of highly potent opioid adulterants (e.g., fentanyl) in the unregulated drug supply, drug checking services (DCS) have emerged as part of a comprehensive approach to overdose prevention. In Canada’s largest city, Toronto, a network of DCS launched in 2019 to prevent overdose and overdose-related risk behaviors. This network employs mass spectrometry technologies, with intake sites co-located with supervised consumption services (SCS) at three frontline harm reduction agencies. The protocol and rationale for assessing the impact of this multi-site DCS network in Toronto is described herein. The aims of this study are to (1) evaluate the impact of DCS access on changes in and factors influencing overdose and related risk behaviors, (2) investigate the perceived capacity of DCS to prevent overdose, and (3) identify composition (qualitative and quantitative) trends in Toronto’s unregulated drug supply. Methods We will use a parallel-mixed-methods design with complementary data sources (including data from chemical analysis of drug samples, quantitative intake and post-test surveys, SCS, coroners, paramedic services, and qualitative interviews), followed by a meta-inference process wherein results from analyses are synthesized. Results Whereas most DCS globally target “recreational drug users,” in Toronto, this networked DCS will primarily target marginalized people who use drugs accessing frontline services, many of whom use drugs regularly and by injection. This evolution in the application of DCS poses important questions that have not yet been explored, including optimal service delivery models and technologies, as well as unique barriers for this population. Increasing information on the unregulated drug supply may modify the risk environment for this population of people who use drugs. Conclusions This study addresses evidence gaps on the emerging continuum of overdose prevention responses and will generate critical evidence on a novel approach to reducing the ongoing high incidence of drug-related morbidity and mortality in Canada and elsewhere.

Target Tracking (TT) is an application of Wireless Sensor Networks (WSNs) which necessitates constant assessment of the location of a target. Any change in position of a target and the distance from each intermediate sensor node to the target is passed on to base station and these factors play a crucial role in further processing. The drawback of WSN is that it is prone to numerous constraints like low power, faulty sensors, environmental noises, etc. The target should be detected first and its path should be tracked continuously as it moves around the sensing region. This problem of detecting and tracking a target should be conducted with maximum accuracy and minimum energy consumption in each sensor node. In this paper, we propose a Target Detection and Target Tracking (TDTT) model for continuously tracking the target. This model uses prelocalization-based Kalman Filter (KF) for target detection and clique-based estimation for tracking the target trajectories. We evaluated our model by calculating the probability of detecting a target based on distance, then estimating the trajectory. We analyzed the maximum error in position estimation based on density and sensing radius of the sensors. The results were found to be encouraging. The proposed KF-based target detection and clique-based target tracking reduce overall expenditure of energy, thereby increasing network lifetime. This approach is also compared with Dynamic Object Tracking (DOT) and face-based tracking approach. The experimental results prove that employing TDTT improves energy efficiency and extends the lifetime of the network, without compromising the accuracy of tracking.

In this work, we attempt to decorate heteroatoms, including nitrogen and sulphur, into activated carbon without post-treatment. These atoms can be introduced into the carbon framework by the strong acid/base reactions after the carbonisation and activation. Raw materials, which contain the desired heteroatoms, were selected so that the obtained activated carbon would have the heteroatom without the harmful chemical process. Crab and shrimp shells were used as the raw material for nitrogen-doped activated carbon since they both contain chitin, which is the organic compound containing nitrogen atom. Similarly, lignite from Mae Moh (Lampang province, Thailand) which has high sulphur content, is suitable for the preparation of sulphur-doped activated carbon. Two types of activating agents, K2CO3 and KOH, were used as the activating agents and the effect from the ratio of raw materials to activating agents were studied. The surface area and the pore structures were determined by nitrogen adsorption analysis and the elemental compositions were characterised by CHNS analysis. It is found that activated carbon prepared from shrimp shells has a higher nitrogen content (1.40 wt%) than that prepared from crab shells (0.47 wt%). The specific area of the obtained activated carbonwas as high as 2,100 m2/g with the optimal condition. Sulphur-doped activated carbon can be easily preparedfrom lignite with high sulphur contents (14.34 wt%) and commercially comparable specific surface area (650 m2/g).

An expedition by scientists participating in a Plant Genetic Conservation Project under the Royal Initiative of Her Royal Highness Princess Maha Chakri Sirindhorn to Tachai Island, off the coast of Changwat Phangnga, Andaman Sea, southern Thailand in April 1999, revealed a new record of the freshwater crab Phricotelphusa callianira (De Man 1887). The new distribution data is considered indicative of a lack of freshwater biota surveys in the area studied, and emphasizes the need for further biogeographical studies.

Field experiment was conducted during kharif seasons of 2009 and 2010 at Agricultural Research Institute, A.N.G.R. Agricultural University, Rajendranagar, Hyderabad to study heat and radiation use efficiency of maize crop. The experiment was in four dates of sowing (7 July, 21 July, 6 August and 22 August in 2009 and 18 June, 02 July, 17 July and 02 August in 2010) with recommended dose of fertilizer (200kg ha-1). Results revealed that the mean AGDD, AHTU and APTU were 1740+29, 9025+1477 and 21155+812, respectively from emergence to physiological maturity. Crop took 110+4 calender days from emergence to physiological maturity. Progressive delay in sowing caused a decrease in AGDD i.e., 1754 to 1717 in 2009 and 1788 to 1746 in 2010. Higher heat use efficiency of 8.44 and 8.42kg oC day-1 for biomass and 4.93 and 4.68 kg oC day-1 for grain and radiation use efficiency of 7.99 and 8.19 kg MJ-1 for biomass and for grain 4.55 and 4.57 kg MJ-1were obtained in crop sown on 21 July in 2009 and 18 June in 2010 respectively.

Abstract This study investigates the impact of environmental factors on the quality changes of mackerel (Scomber japonicus), a globally significant fish species valued for its high nutritional content, especially its unsaturated fatty acids and proteins. In South Korea, marine products like mackerel undergo auction, sorting, packaging, and transportation. Assessing the fish freshness and quality during these processes, before it reaches consumers, is crucial. This research aimed to track the changes in mackerel quality indicators after the auction and correlate them with the environmental factors affecting transportation. Using a transportation model established for both summer and winter, mackerel samples were categorized by their weight (200 and 300 g). Results show poorer quality indicators in summer compared to winter, particularly in the 200 g group. Microbiological indicators such as the total viable count (0.923) and total coliforms (0.958) were most strongly correlated with the season, whereas the lipid oxidation indicators acid value (0.627) and peroxide value (0.536) were moderately correlated with the season. Interestingly, the wider standard deviation distribution observed in summer suggests that individual mackerel characteristics may influence the degree of quality changes. These findings offer insights into mackerel quality fluctuations during auction and transportation, aiding in future efforts to maintain seafood quality amidst climate change.

Purpose: Semantic segmentation is a fundamental part of the surgical application of deep learning. Traditionally, segmentation in vision tasks has been performed using convolutional neural networks (CNNs), but the transformer architecture has recently been introduced and widely investigated. We aimed to investigate the performance of deep learning models in segmentation in robot-assisted radical prostatectomy (RARP) and identify which of the architectures is superior for segmentation in robotic surgery. Materials and Methods: Intraoperative images during RARP were obtained. The dataset was randomly split into training and validation data. Segmentation of the surgical instruments, bladder, prostate, vas and seminal vesicle was performed using three CNN models (DeepLabv3, MANet, and U-Net++) and three transformers (SegFormer, BEiT, and DPT), and their performances were analyzed. Results: The overall segmentation performance during RARP varied across different model architectures. For the CNN models, DeepLabV3 achieved a mean Dice score of 0.938, MANet scored 0.944, and U-Net++ reached 0.930. For the transformer architectures, SegFormer attained a mean Dice score of 0.919, BEiT scored 0.916, and DPT achieved 0.940. The performance of CNN models was superior to that of transformer models in segmenting the prostate, vas, and seminal vesicle. Conclusions: Deep learning models provided accurate segmentation of the surgical instruments and anatomical structures observed during RARP. Both CNN and transformer models showed reliable predictions in the segmentation task; however, CNN models may be more suitable than transformer models for organ segmentation and may be more applicable in unusual cases. Further research with large datasets is needed.

Objective We investigated genetic parameters of calving ease (CE) using several animal models in Korean Holstein and searched for suitable models for routine evaluation of CE. Methods Two phenotypic datasets of CE (DS5 and DS10) on first-parity Korean Holstein calves were prepared. DS5 and DS10 included at least 5 and 10 CE records per herd-year level and comprised 117,921 and 80,389 observations, respectively. The CE phenotypes ranged from 1 to 4, from a normal to extreme difficulty calving scale. The CE was defined as a trait of the calf. The BLUPF90+ software was used for (co)variances estimation through four animal models with a maternal effect (M1 to M4), where all models included effects of a fixed calf-sex, a fixed dam calving age (covariate), and one or more fixed contemporary group (CG) terms. The CG effects were different across models—a herd-year-season (M1, HYS), a herd-year and year-season (M2, HY+YS), a herd-year and season (M3, HY+S), and a herd and year-season (M4, H+YS). Results Direct heritability (h2) estimates of CE ranged from 0.005 to 0.234 across models and datasets. Maternal h2 values were low (0.001 to 0.090). Genetic correlations between direct and maternal effects were strongly negative to lowly positive (−0.814 to 0.078), further emphasizing its importance in CE evaluation models. These genetic parameter estimates also indicate slower future selection progress of CE in Korean Holsteins. The M1 fitted many levels with fewer observations per level deriving unreliable parameters, and the M4 did not account for confounded herd and animal structures. The M2 and M3 were deemed more realistic for implementation, and they were better able to account for data structure issues (incompleteness and confounding) than other models. Conclusion As the pioneering study to employ animal models in Korean Holstein CE evaluation, our findings hold significant potential for this breed’s future and routine evaluation development.

The Geostationary Environment Monitoring Spectrometer (GEMS) on board the GEO-KOMPSAT-2B satellite is the first geostationary satellite launched to monitor the environment. GEMS conducts hourly measurements during the day over eastern and southeastern Asia. This work presents glyoxal (CHOCHO) vertical column densities (VCDs) retrieved from GEMS, with optimal settings for glyoxal retrieval based on sensitivity tests involving reference spectrum sampling and fitting window selection. We evaluated GEMS glyoxal VCDs by comparing them to the TROPOspheric Monitoring Instrument (TROPOMI) and multi-axis differential optical absorption spectroscopy (MAX-DOAS) ground-based observations. On average, GEMS and TROPOMI VCDs show a spatial correlation coefficient of 0.63, increasing to 0.87 for northeastern Asia. While GEMS and TROPOMI demonstrate similar monthly variations in the Indochinese Peninsula regions (R > 0.67), variations differ in other areas. Specifically, GEMS VCDs are higher in the winter and either lower or comparable to TROPOMI and MAX-DOAS VCDs in the summer across northeastern Asia. We attributed the discrepancies in the monthly variation to a polluted reference spectrum and high NO2 concentrations. When we correct GEMS glyoxal VCDs as a function of NO2 SCDs, the monthly correlation coefficients substantially increase from 0.16–0.40 to 0.45–0.72 in high NO2 regions. When averaged hourly, GEMS and MAX-DOAS VCDs exhibit similar diurnal variations, especially at stations in Japan (Chiba, Kasuga, and Fukue).

MltG, a membrane-bound lytic transglycosylase, has roles in terminating glycan polymerization in peptidoglycan and incorporating glycan chains into the cell wall, making it significant in bacterial cell-wall biosynthesis and remodeling. This study provides the first reported MltG structure from Mycobacterium abscessus (maMltG), a superbug that has high antibiotic resistance. Our structural and biochemical analyses revealed that MltG has a flexible peptidoglycan-binding domain and exists as a monomer in solution. Further, the putative active site of maMltG was disclosed using structural analysis and sequence comparison. Overall, this study contributes to our understanding of the transglycosylation reaction of the MltG family, aiding the design of next-generation antibiotics targeting M. abscessus.

Background: This study aimed to evaluate the long-term impact of postoperative prolonged air leak (PAL) on pulmonary function. Methods: We enrolled 1,316 patients with pathologic stage I–III lung cancer who underwent lobectomy. The cohort was divided into 2 groups: those who experienced PAL (n=55) and those who did not (n=1,261). Propensity score matching was conducted at a 1:4 ratio, resulting in 49 patients in the PAL group and 189 in the non-PAL group. Changes in pulmonary function were compared among preoperative, 6-month postoperative, and 12-month postoperative measurements between the 2 groups. Results: The variables used for propensity score matching included age, sex, smoking history, body mass index, baseline pulmonary function, pathologic stage, and surgical approach. All standardized mean differences were less than 0.1. Six months postoperatively, the PAL group showed a greater reduction in both forced expiratory volume in 1 second (FEV1) (-13.0% vs. -10.0%, p=0.041) and forced vital capacity (FVC) (-15.0% vs. -9.0%, p

Abstract Background and aims The association between metabolic dysfunction-associated steatotic liver disease (MASLD) and cardiovascular outcomes in patients with type 2 diabetes mellitus (T2DM) is unclear. This study aimed to investigate the impact of spectrum of SLD on the risk of heart failure and cardiovascular (CV) mortality in patients with T2DM. Methods In a nationwide cohort study, 2,745,689 adults with T2DM were followed from 2009 to 2012 until 2018. Participants were categorized into no steatotic liver disease (no SLD) and SLD groups. The SLD group was stratified based on metabolic risk factors, alcohol consumption and viral hepatitis. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for heart failure (HF) and CV mortality risk. Results The prevalence of MASLD, metabolic alcohol-associated liver disease (MetALD), alcohol-associated liver disease with metabolic dysfunction (ALD with MD) and MASLD with viral hepatitis (VH) was 49.6%, 7.2%, 2.3%, and 2.0%. Individuals with MASLD (adjusted HR [aHR], 1.11), MetALD (aHR, 1.14), ALD with MD (aHR, 1.32) and MASLD with VH (aHR, 1.12) had a higher risk of developing HF compared with the no SLD group. The risk of CV mortality was also increased in those with SLD groups compared to those with no SLD. The risk of new-onset HF and CV mortality showed a J-shaped association with alcohol consumption regardless of SLD status. Conclusion SLD is independent risk factor of new-onset HF and CV mortality in persons with T2DM, and alcohol consumption has a J-shaped association with risk of HF and CV mortality, regardless of SLD status.

Abstract Background To investigate the outcomes of bilateral implantation of enhanced monofocal intraocular lenses (IOLs, ICB00) with a − 2.00 diopter (D) target in patients with moderate to high myopia and to compare the clinical outcomes of a − 2.00 D binocular target with an emmetropia target in patients who underwent cataract surgery. Methods In this retrospective study, we reviewed the medical records of patients who underwent uncomplicated phacoemulsification with ICB00 IOL implantation. Emmetropia (Group 1) and − 2.00 D (Group 2) were targeted in 60 and 20 eyes of 30 and 10 patients, respectively. Three months after surgery, uncorrected distance visual acuity (UDVA), corrected distance visual acuity (CDVA), uncorrected intermediate visual acuity (UIVA), and uncorrected near visual acuity (UNVA) were measured. Defocus curves were measured under the photopic condition by intervals of 0.50 D from + 0.50 D to − 4.00 D. Results The postoperative binocular logMAR UDVA, UIVA, and UNVA were 0.01 ± 0.03, 0.08 ± 0.11, and 0.33 ± 0.15 in Group 1 and 0.31 ± 0.13, 0.04 ± 0.05, and 0.11 ± 0.07 in Group 2, respectively. Group 2 showed a significantly superior postoperative binocular UNVA (P = 0.027) and inferior binocular UDVA (P = 0.003) than Group 1. Binocular UIVA and CDVA did not significantly differ between the groups although UIVA was better in Group 2 than in Group 1. Near glasses were needed by 66% of Group 1 and 0% of Group 2. Conclusions Bilateral implantation of ICB00 IOL with − 2.00 D of residual myopia is suitable for patients with moderate to high myopia to improve UDVA, UIVA, and UNVA.

Abstract Guidelines encouraging social distancing, limited outings, and remote work due to COVID-19 have increased sedentary periods and reduced levels of physical activity. These habits increase the risk of metabolic diseases, obesity, cardiovascular disease, and diabetes mellitus. The World Health Organization recommends muscle-strengthening exercises as well as regular physical activity to promote overall health. This study investigates the effect of a wearable hip exoskeleton on muscle activity and heart rate during anaerobic exercise in 40 healthy adults (mean age of 40.00 ± 11.51 years; n = 20 females). Bot Fit, a wearable hip-type robotic exoskeleton, was developed by Samsung Electronics Co., Ltd. (Suwon, Republic of Korea) to enhance the effects of both aerobic and anaerobic exercise. All study participants performed a fitness exercise protocol, including knee-ups, good mornings, squats, mountain climbs, kick-backs, reverse lunges, and split jacks, with and without a Bot Fit. To evaluate the effect of anaerobic exercise with the Bot Fit, muscle activity and heart rate were measured during fitness exercises with and without a Bot Fit. Measured muscles were the rectus abdominis (RA), erector spinae (ES), rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM), biceps femoris (BF), tibialis anterior (TA), gastrocnemius medialis (GCM), and gluteus maximus (GM). During anaerobic exercises with the Bot Fit, there was a significant increase in muscle activity compared to exercising without the Bot Fit. Muscle activity increased significantly in the RA, RF, VL, BF and TA muscles during knee-ups; in the ES, BF, and GM during good mornings; in the RF, VL, VM, BF, and GM during squats; in RA, RF, VL, VM, and GM during mountain climbs; in the RA and BF during kick-backs; in the RF, BF, and GCM during reverse lunges; and in the RF and VL during split jacks (p

Abstract Acanthopanax senticosus belongs to Araliaceae family and is traditionally used as a tonic. The roots and stems are mainly used as treatments for hypodynamia, rheumatism, and hypertension, but their frequent use may lead to extinction. However, comprehensive and simultaneous analysis of the remaining parts were still limited. There is a need to reorganize them for standardization of functional foods. In this study, 50 phenolic compounds and 82 triterpenoid saponins from the shoots, leaves, fruits, and stems of were characterized using UPLC-QTOF-MS and UPLC-QTRAP-MS/MS. Among them, 52 compounds were newly determined as the cis and malonyl-bound phenolic acids and were found to be structural isomers of Acanthopanax flavonoids and saponins. All compounds were absolutely/relatively quantified, and shoots had the highest content. Peroxynitrite and α-glucosidase inhibitory activities were performed, followed by evaluation of structure-activity relationships. Particularly, hederasaponin B and ciwujianoside B showed remarkable efficacy, which were affected by the C-23 hydroxylation, the C-20(29) double bond, and the presence of rhamnose. These detailed profiling can be used as fundamental data for increasing the utilization of A. senticosus and developing them into functional foods.

Abstract Cerebral hyperperfusion syndrome (CHS) is a serious complication after bypass surgery in Moyamoya disease (MMD), with autoregulatory dysfunction being a major pathogenesis. This study investigated the change of perioperative autoregulation and preoperative prognostic potentials in MMD with postoperative CHS. Among 26 hemispheres in 24 patients with adult MMD undergoing combined bypass, 13 hemispheres experienced postoperative CHS. Arterial blood pressure and cerebral blood flow velocity were perioperatively measured with transcranial Doppler ultrasound during resting and the Valsalva maneuver (VM). Autoregulation profiles were discovered in both the CHS and non-CHS groups using mean flow index (Mxa), VM Autoregulatory Index (VMAI), and a new metric termed VM Overshooting Index (VMOI). The CHS group had inferior autoregulation than the non-CHS group as indicated by VMOI on preoperative day 1 and postoperative 3rd day. Deteriorated autoregulation was observed via Mxa in the CHS group than in the non-CHS group on the postoperative 3rd and discharge days. Postoperative longitudinal autoregulation recovery in the CHS group was found in a logistic regression model with diminished group differences over the time course. This work represents a step forward in utilizing autoregulation indices derived from physiological signals, to predict the postoperative CHS in adult MMD.