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Length of Day (LOD) observations in the range 720 BCE to 2020—derived from lunar occultation and eclipse records—feature a secular trend and various long‐period fluctuations. While recent estimates show that the secular trend is caused by the combination of lunar tidal friction and glacial isostatic adjustment, the causes of long‐period fluctuations remain ambiguous. We first compute the climatic effects and show that they are anti‐correlated with the observed fluctuations and their amplitude is ∼ ${\sim} $10 smaller. Then, we focus on core dynamics and solve for simplified equations of magnetohydrodynamics, namely tangential geostrophy, using Bayesian Physics‐Informed Neural Networks (BPINNs) and independent archeomagnetic and modern geomagnetic observations. Within the observation and reconstruction uncertainty we can reconcile the LOD observations with reconstructions of BPINNs. Furthermore, we demonstrate that LOD variations reconstructed by dynamics of Magneto‐Archimedes‐Coriolis waves do not explain the observed fluctuations. These results have considerable implications for internal and external geodynamics. Plain Language Summary: The Length of Day (LOD) is variable over time, deviating from its nominal value of 86,400 s. In the available observational period—720 BCE to 2020—LOD features decadal and millennial fluctuations, as well as a secular trend. Recent studies have shown that the secular trend is caused by a combination of (a) Moon's gravitational pulling on the Earth and subsequent energy dissipation mainly in the Earth's oceans, and (b) solid Earth rebound due to the Earth's adjustment after the termination of the last ice age. The causes of fluctuations are not known precisely. One possibility is that they are driven by climatic oscillations in the past three thousand years, which we discount here, however, on the grounds that they are anti‐correlated with and too small to account for the observed fluctuations. We then show that these fluctuations can be explained—within the uncertainty—by Bayesian physics‐informed neural networks based on simple principles of Earth's core magnetohydrodynamics, and independent archeomagnetic and more modern geomagnetic observations. Key Points: We analyze the variations in Length of Day (LOD) in the range 720 BCE to 2020 using Bayesian Physics‐Informed Neural Networks (BPINNs)We use simplified equations of magnetohydrodynamics to represent the core surface flow and archeomagnetic and modern geomagnetic dataWe show that the LOD reconstructed by BPINNs can be reconciled with observations within the observational uncertainty [ABSTRACT FROM AUTHOR]

Biochar is the product of intentional pyrolysis of organic feedstocks. It is made under controlled conditions in order to achieve desired physico‐chemical characteristics. These characteristics ultimately affect biochar properties as a soil amendment. When biochar is used for carbon storage, an important property is its persistence in soil, often described by the proportion of biochar carbon remaining in soil after a 100 years (Fperm$$ {\mathrm{F}}_{\mathrm{perm}} $$). We analyzed published data on 1230 biochars to re‐evaluate the effect of pyrolysis parameters on biochar characteristics and the possibility to predict Fperm$$ {\mathrm{F}}_{\mathrm{perm}} $$ from the maximum temperature reached during pyrolysis (HTT). We showed that biochar ash and nitrogen (N) contents were mostly affected by feedstock type. The oxygen to carbon (O:C) and hydrogen to carbon (H:C) ratios were mostly affected by the extent of pyrolysis (a combination of HTT and pyrolysis duration), except for non (ligno)cellulosic feedstocks (plastic waste, sewage sludge). The volatile matter (VM) content was affected by both feedstock type and the extent of pyrolysis. We demonstrated that HTT is the main driver of H:C ‐‐ an indicator of persistence ‐‐ but that it is not measured accurately enough to precisely predict H:C, let alone persistence. We examined the equations to estimate Fperm$$ {\mathrm{F}}_{\mathrm{perm}} $$ available in the literature and showed that Fperm$$ {\mathrm{F}}_{\mathrm{perm}} $$ calculated from HTT presented little agreement with Fperm$$ {\mathrm{F}}_{\mathrm{perm}} $$ calculated from H:C. The sign and magnitude of the bias depended on the equation used to calculate Fperm$$ {\mathrm{F}}_{\mathrm{perm}} $$ and the dispersion was usually large. This could lead to improper compensation of carbon emissions and wrong reporting of carbon sinks in national carbon accounting schemes. We recommend not to use HTT as a predictor for persistence and stress the importance to rapidly develop more accurate proxies of biochar C persistence in soil. [ABSTRACT FROM AUTHOR]

Earth possesses a Poincaré mode called Free Core Nutation (FCN) due to the misalignment of the rotation axes of the mantle and fluid outer core. FCN is the primary signal in the observations of Celestial Pole Offsets (CPO) and maintained by geophysical mechanisms that are yet to be understood. Earlier studies suggested an origin in Atmospheric Angular Momentum (AAM)—and to a lesser degree Oceanic Angular Momentum (OAM)—but discrepancies between these geophysical excitations and the geodetic (CPO‐based) excitation were too large to reach definite conclusions. Here we use newly calculated, 3‐hourly AAM and OAM series for the 1994–2022 period, in conjunction with the latest CPO series from the International Earth Rotation and Reference Systems Service (IERS 20 C04 series), to demonstrate a markedly lower power ratio (∼ ${\sim} $4.6) of geophysical over geodetic excitation at the FCN frequency compared to previous works (ratio ∼ ${\sim} $10). Among all excitation sources, the AAM pressure term exhibits the highest coherence (0.56) and correlation (0.48) with the geodetic excitation, whereas the coherence with OAM is smaller by a factor of 3. Similar analyses using existing angular momentum series give comparable, albeit smaller coherence and correlation results. We attribute the relevant AAM pressure term signal to Northern Hemispheric landmasses and further show consistent temporal variations in the amplitude of geophysical and geodetic excitations around the FCN band. Our results thus corroborate evidence for large‐scale atmospheric mass redistribution to be the main cause of continuous FCN excitation. Plain Language Summary: We revisit the excitation of Free Core Nutation (FCN) by generating new effective Atmospheric and Oceanic Angular Momentum (AAM and OAM, respectively) time series since 1994 up to the end of 2022. We follow the deconvolution approach and compute the geophysical excitation based on complex demodulation at retrograde diurnal frequency band, as well as geodetic excitation from a digital filter based on broad‐band Liouville equation. We show that geodetic and geophysical excitations exhibit consistent patterns of temporal variation, having significant coherence and correlation with largest contribution from the pressure term of AAM. Comparison between our new AAM and OAM series with those of European Center for Medium‐Range Weather Forecasts show that our series are more correlated to the geodetic excitation. These findings confirm the role of atmosphere on the excitation of FCN and can be used to more accurately model and predict the FCN variations, thereby enhancing the nutation theory. Key Points: We revisit the excitation of Free Core Nutation (FCN) based on new effective angular momentum functions and geophysical considerationsThe pressure term of atmospheric angular momentum, particularly over Northern Hemispheric landmasses, is most effective in exciting the FCNWavelet analysis reveals similar temporal variations between observed and modeled FCN excitations from 1994 to 2022 [ABSTRACT FROM AUTHOR]

Increasing impacts of wildfires on Western US air quality highlights the need for forecasts of smoke emissions based on dynamic modeled wildfires. This work utilizes knowledge of weather, fuels, topography, and firefighting, combined with machine learning and other statistical methods, to generate 1‐ and 2‐day forecasts of fire radiative energy (FRE). The models are trained on data covering 2019 and 2021 and evaluated on data for 2020. For the 1‐day (2‐day) forecasts, the random forest model shows the most skill, explaining 48% (25%) of the variance in observed daily FRE when trained on all available predictors compared to the 2% (<0%) of variance explained by persistence for the extreme fire year of 2020. The random forest model also shows improved skill in forecasting day‐to‐day increases and decreases in FRE, with 28% (39%) of observed increase (decrease) days predicted, and increase (decrease) days are identified with 62% (60%) accuracy. Error in the random forest increases with FRE, and the random forest tends toward persistence under severe fire weather. Sensitivity analysis shows that near‐surface weather and the latest observed FRE contribute the most to the skill of the model. When the random forest model was trained on subsets of the training data produced by agencies (e.g., the Canadian or US Forest Services), comparable if not better performance was achieved (1‐day R2 = 0.39–0.48, 2‐day R2 = 0.13–0.34). FRE is used to compute emissions, so these results demonstrate potential for improved fire emissions forecasts for air quality models. Plain Language Summary: Increasing wildfire smoke is undoing decades of air quality progress, yet air quality forecasts often miss the most intense smoke events. This is because forecasted smoke is released at constant rates whereas the rate of smoke release from real wildfires varies in time. In this work we teach a machine learning algorithm to predict the daily change in fire heat output, a quantity that is used to calculate wildfire emissions. The machine learning algorithm uses information regarding weather, fuel moisture and amount, and firefighting efforts to make its predictions. We also tried to predict the daily change in fire heat output using only weather information but found the machine learning method to be more successful. Many federal agencies have their own ways of tracking fire weather and fuel moisture, and in this paper, we show that we can apply machine learning to the data from any of several agencies and get the same level of forecasting skill. Key Points: Random forest models trained on weather, fuel, and firefighting data surpass persistence and weather‐based methods to predict fire energyRandom forest models beat persistence across states, for most days in the 2020 fire season, and across levels of fire severityFire weather and latest fire energy predictors add most skill to the random forest; models using agency weather data perform similarly [ABSTRACT FROM AUTHOR]

Background: Sleep plays an important role in neurodevelopment. However, the effects of prenatal alcohol exposure on sleep quality have been understudied, despite reports of sleep disturbance in infants prenatally exposed to alcohol and elevated levels of sleep problems reported by caregivers of children with fetal alcohol spectrum disorders. The current study characterizes sleep in children with prenatal alcohol exposure using both objective (actigraphy) and subjective (questionnaires, sleep diaries) methods. Methods: Participants aged 6–10 years, with and without prenatal alcohol exposure, were included in the study (alcohol‐exposed [AE]: n = 35; control [CON]: n = 39). Objective sleep was measured via 24‐h actigraphy for 2 weeks. Parents completed sleep diaries and sleep questionnaires (Children's Sleep Habits Questionnaire, Pediatric Sleep Questionnaire). Multivariate analysis of variance was used to characterize the sleep profile (objective, subjective) and examine group differences. Results: There were no group differences on actigraphy metrics averaged across 2 weeks. However, the AE group showed significantly greater intraindividual variability on most actigraphy measures, particularly total sleep time, percent sleep, wake after sleep onset, and number of wake bouts. Parents reported significantly more sleep problems in the AE group than in the CON group, primarily driven by night wakings, parasomnias (e.g., sleepwalking), snoring, and daytime sleepiness. These effects were more severe in children >8.5 years of age. Conclusions: Despite similar 2‐week average sleep outcomes, children with prenatal alcohol exposure showed greater intraindividual sleep variability and parents reported more sleep problems related to sleep behavior and snoring. These difficulties with sleep may be related to other cognitive and behavioral outcomes. Importantly, sleep is a modifiable behavior, and interventions that focus on variability in sleep, particularly in sleep duration, can impact the quality of life in children with prenatal alcohol exposure and their families. [ABSTRACT FROM AUTHOR]

Prescribed fires (fires intentionally set for mitigation purposes) produce pollutants, which have negative effects on human and animal health. One of the pollutants produced from fires is fine particulate matter (PM2.5). The Flint Hills (FH) region of Kansas experiences extensive prescribed burning each spring (March‐May). Smoke from prescribed fires is often understudied due to a lack of monitoring in the rural regions where prescribed burning occurs, as well as the short duration and small size of the fires. Our goal was to attribute PM2.5 concentrations to the prescribed burning in the FH. To determine PM2.5 increases from local burning, we used low‐cost PM2.5 sensors (PurpleAir) and satellite observations. The FH were also affected by smoke transported from fires in other regions during 2022. We separated the transported smoke from smoke from fires in eastern Kansas. Based on data from the PurpleAir sensors, we found the 24‐hr median PM2.5 to increase by 3.0–5.3 μg m−3 (based on different estimates) on days impacted by smoke from fires in the eastern Kansas region compared to days unimpacted by smoke. The FH region was the most impacted by smoke PM2.5 compared to other regions of Kansas, as observed in satellite products and in situ measurements. Additionally, our study found that hourly PM2.5 estimates from a satellite‐derived product aligned with our ground‐based measurements. Satellite‐derived products are useful in rural areas like the FH, where monitors are scarce, providing important PM2.5 estimates. Plain Language Summary: Prescribed fires (fires intentionally set for land management) produce harmful air pollutants, including fine particulate matter (PM2.5). In the Flint Hills (FH) of Kansas, smoke from prescribed fires has not been studied enough. This is because there are fewer PM2.5 monitors in rural areas, and the fires are short‐lived. Our goal was to figure out how much PM2.5 comes from these prescribed burns in the FH, using low‐cost sensors (PurpleAir) and observations from satellites. The FH also experienced smoke from fires in other places in 2022. We separated the smoke from fires outside eastern Kansas from smoke that came from fires in eastern Kansas. The PurpleAir sensors showed that on days affected by smoke from eastern Kansas fires, the PM2.5 concentration increased between 3.0 and 5.3 μg m−3 compared to days without smoke. The FH was the most impacted region compared to other regions of Kansas, highlighting the need to better understand and address the health risks associated with prescribed fires. We also found that our PM2.5 measurements in the FH are similar to satellite‐based estimates. Satellite data fills in the gaps between the monitors, especially in rural areas such as the FH. Key Points: The Flint Hills of Kansas are impacted by prescribed burning each spring; there were 2.1 million acres burned in 2022Particulate matter (PM2.5) concentrations substantially increased on days impacted by smoke from fires in eastern Kansas compared to days without smokePM2.5 from smoke reaches a maximum overnight in eastern Kansas; this time period is not captured by satellite observations [ABSTRACT FROM AUTHOR]

Radio telescopes with dual linearly polarized feeds regularly participate in Very Long Baseline Interferometry. One example is the VLBI Global Observing System (VGOS), which is employed for high‐precision geodesy and astrometry. In order to achieve the maximum signal‐to‐noise ratio, the visibilities of all four polarization products are combined to Stokes I before fringe‐fitting. Our aim is to improve cross‐polarization bandpass calibration, which is an essential processing step in this context. Here we investigate the shapes of these station‐specific quantities as a function of frequency and time. We observed the extra‐galactic source 4C 39.25 for 6 hours with a VGOS network. We correlated the data with the DiFX software and analyzed the visibilities with PolConvert to determine the complex cross‐bandpasses with high accuracy. Their frequency‐dependent shape is to first order characterized by a group delay between the two orthogonal polarizations, in the order of several hundred picoseconds. We find that this group delay shows systematic variability in the range of a few picoseconds, but can remain stable within this range for several years, as evident from earlier sessions. On top of the linear phase‐frequency relationship there are systematic deviations of several tens of degrees, which in addition are subject to smooth temporal evolution. The antenna cross‐bandpasses are variable on time scales of ∼1 hr, which defines the frequency of necessary calibrator scans. The source 4C 39.25 is confirmed as an excellent cross‐bandpass calibrator. Dedicated surveys are highly encouraged to search for more calibrators of similar quality. Key Points: The new‐generation geodetic radio telescopes observe two orthogonal linear polarization directionsCalibration of the gain differences between the two polarizers is necessary to maximize the signal‐to‐noise ratio of observationsWe investigate these cross‐polarization gain differences and their temporal evolution for selected antennas [ABSTRACT FROM AUTHOR]

Chronic liver diseases are worldwide on the rise. Due to the rapidly increasing incidence, in particular in Western countries, metabolic dysfunction-associated steatotic liver disease (MASLD) is gaining importance as the disease can develop into hepatocellular carcinoma. Lipid accumulation in hepatocytes has been identified as the characteristic structural change in MASLD development, but molecular mechanisms responsible for disease progression remained unresolved. Here, we uncover in primary hepatocytes from a preclinical model fed with a Western diet (WD) an increased basal MET phosphorylation and a strong downregulation of the PI3K-AKT pathway. Dynamic pathway modeling of hepatocyte growth factor (HGF) signal transduction combined with global proteomics identifies that an elevated basal MET phosphorylation rate is the main driver of altered signaling leading to increased proliferation of WD-hepatocytes. Model-adaptation to patient-derived hepatocytes reveal patient-specific variability in basal MET phosphorylation, which correlates with patient outcome after liver surgery. Thus, dysregulated basal MET phosphorylation could be an indicator for the health status of the liver and thereby inform on the risk of a patient to suffer from liver failure after surgery. Synopsis: Primary hepatocytes from a preclinical mouse model fed with high-sugar and high-fat diet are used to develop a data-based mathematical model that identifies the basal phosphorylation rate of the tyrosine kinase receptor MET as the main dysregulated parameter driving fatty liver disease. An increase in phosphorylated MET and a strong downregulation of the PI3K-AKT pathway in response to hepatocyte growth factor (HGF) stimulation are hallmarks of primary hepatocytes derived from mice exposed to a high-fat and high-sugar "Western" diet. Dynamic pathway modeling of HGF signal transduction combined with proteomics identifies elevated basal MET phosphorylation rate as the main driver of altered signaling and increased proliferation. Model-adaptation to primary human hepatocytes reveals that patient-specific variability in basal MET phosphorylation correlates with patient outcome after liver surgery. Primary hepatocytes from a preclinical mouse model fed with high-sugar and high-fat diet are used to develop a data-based mathematical model that identifies the basal phosphorylation rate of the tyrosine kinase receptor MET as the main dysregulated parameter driving fatty liver disease. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems (GNSS) are not only a state‐of‐the‐art sensor for positioning and navigation applications but also a valuable tool for remote sensing. Through the usage of L‐band carrier frequencies, GNSS acts as an all‐weather‐operation system, offering substantial benefits compared to optical systems. Nevertheless, severe weather can still have an impact on the strength of signals received at a ground station, as we show in this study. We investigate GNSS Signal‐to‐Noise Ratio (SNR) observations during two severe convective storm events over the city of Zurich, Switzerland. We make use of a GNSS‐SNR‐based algorithm originally developed for the detection of hail particles from volcanic eruptions. Results indicate that, although GNSS observations are considered to be fairly insensitive to the presence of hydrometeors, convective storm events are visible in SNR observations. SNR levels of affected satellites show a significant drop during event periods, which are determined by weather radar observations. Plain Language Summary: Over the last two decades, observations from Global Navigation Satellite Systems (GNSS) have proven to be a useful data source for meteorology. Typically, signal delays introduced by the presence of water vapor along the signal path are utilized for analyzing and predicting the atmospheric moisture field, and subsequently, precipitation. However, other GNSS observations types can also be influenced by severe thunder‐ and hailstorms, which are high‐impact weather events. In this study, we show that is the case for the Signal‐to‐Noise Ratio (SNR) of GNSS signals. We investigate two large thunderstorm events which took place over the city of Zurich. These events are visible as significant degradation in SNR data of GNSS satellites. By analyzing radar images we are able to show that the time period of SNR degradation closely corresponds to the period of strongest precipitation intensity observed by radar. Although more detailed investigations have to be carried out in the future, these initial findings indicate the potential of GNSS‐SNR data for observation of severe weather events and strengthen the status of GNSS as a valuable tool for meteorological applications. Key Points: GNSSSevere storm detectionSignal‐to‐Noise ratio [ABSTRACT FROM AUTHOR]

Machine learning (ML) has been increasingly applied to space weather and ionosphere problems in recent years, with the goal of improving modeling and forecasting capabilities through a data‐driven modeling approach of nonlinear relationships. However, little work has been done to quantify the uncertainty of the results, lacking an indication of how confident and reliable the results of an ML system are. In this paper, we implement and analyze several uncertainty quantification approaches for an ML‐based model to forecast Vertical Total Electron Content (VTEC) 1‐day ahead and corresponding uncertainties with 95% confidence intervals (CI): (a) Super‐Ensemble of ML‐based VTEC models (SE), (b) Gradient Tree Boosting with quantile loss function (Quantile Gradient Boosting, QGB), (c) Bayesian neural network (BNN), and (d) BNN including data uncertainty (BNN + D). Techniques that consider only model parameter uncertainties (a and c) predict narrow CI and over‐optimistic results, whereas accounting for both model parameter and data uncertainties with the BNN + D approach leads to a wider CI and the most realistic uncertainties quantification of VTEC forecast. However, the BNN + D approach suffers from a high computational burden, while the QGB approach is the most computationally efficient solution with slightly less realistic uncertainties. The QGB CI are determined to a large extent from space weather indices, as revealed by the feature analysis. They exhibit variations related to daytime/nightime, solar irradiance, geomagnetic activity, and post‐sunset low‐latitude ionosphere enhancement. Plain Language Summary: Space weather describes the varying conditions in the space environment between the Sun and Earth that can affect satellites and technologies on Earth, such as navigation systems, power grids, radio, and satellite communications. The manifestation of space weather in the ionosphere can be characterized using the Vertical Total Electron Content (VTEC) derived from Global Navigation Satellite Systems observations. In this study, the machine learning (ML) approach is applied to approximate the nonlinear relationships of Sun‐Earth processes using data on solar activity, solar wind, magnetic field, and VTEC. However, the measurements and the modeling approaches are subject to errors, increasing the uncertainty of the results when forecasting future instances. For reliable forecasting, it is necessary to quantify the uncertainties. Quantifying the uncertainty is also helpful for understanding the ML‐based model and the problem of VTEC and space weather forecasting. Therefore, in this study, ML‐based models are developed to forecast VTEC within the ionosphere, including the manifestation of space weather, while the degree of reliability is quantified with a target value of 95% confidence. Key Points: Machine learning‐based Vertical Total Electron Content models with 95% confidence intervals (CI) are developed for the first time using four approaches to quantify uncertaintiesBayesian Neural Network quantifying model and data uncertainties contains ground truth within CIs, but is computationally intensiveQuantile Gradient Boosting is fastest with comparable performance in terms of uncertainty; CIs largely determined from space weather indices [ABSTRACT FROM AUTHOR]

Rapid provision of Earth orientation parameters (EOPs, here polar motion and dUT1) is indispensable in many geodetic applications and also for spacecraft navigation. There are, however, discrepancies between the rapid EOPs and the final EOPs that have a higher latency but the highest accuracy. To reduce these discrepancies, we focus on a data‐driven approach, present a novel method named ResLearner, and use it in the context of deep ensemble learning. Furthermore, we introduce a geophysically constrained approach for ResLearner. We show that the most important geophysical information to improve the rapid EOPs is the effective angular momentum functions of atmosphere, ocean, land hydrology, and sea level. In addition, semidiurnal, diurnal, and long‐period tides coupled with prograde and retrograde tidal excitations are important features. The influence of some climatic indices on the prediction accuracy of dUT1 is discussed, and El Niño Southern Oscillation is found to be influential. We developed an operational framework, providing the improved EOPs on a daily basis with a prediction window of 63 days to fully cover the latency of final EOPs. We show that under the operational conditions and using the rapid EOPs of the International Earth Rotation and Reference Systems Service (IERS), we achieve improvements as high as 60%, thus significantly reducing the differences between rapid and final EOPs. Furthermore, we discuss how the new final series IERS 20 C04 is preferred over 14 C04. Finally, we compare against EOP hindcast experiments of the European Space Agency, on which ResLearner presents comparable improvements. Plain Language Summary: The International Earth Rotation and Reference Systems Service (IERS) provides rapid Earth orientation parameters (EOPs) using different space‐geodetic techniques to bridge the latency of the final, most accurate EOPs solution. However, these rapid EOPs are not in full agreement with the final EOPs. In order to reduce the differences between the rapid and final EOPs, we focus on the application of machine learning and present a novel method named ResLearner, which is based on geodetic data and geophysical constraints. We present the method in the context of deep ensemble learning, focusing on a prediction window of 63 days. We also attempt to link informative geophysical effects to these discrepancies. We show that they are linked to a mixture of atmospheric, oceanic, hydrological, and sea level effective angular momentum functions, dominance of the Global Navigation Satellite Systems‐derived polar motion, and various short‐ and long‐term tidal excitations. El Niño Southern Oscillation is also relevant for dUT1 prediction. The methodology can provide significant improvements of up to 60% in operational settings with respect to rapid EOPs provided by IERS. Additional validation is done by using the data of Jet Propulsion Laboratory final EOP series and also EOP series provided by the European Space Agency. Key Points: We introduce a novel machine learning algorithm named ResLearner to improve the accuracy of rapid and predicted Earth orientation parameters (EOPs)We also present geophysically constrained ResLearner, using Earth's effective angular momentum functions, tides, and climatic indicesBesides prediction, ResLearner is also able to effectively correct deficits in rapidly processed EOPs with respect to final EOPs [ABSTRACT FROM AUTHOR]

Agricultural and prescribed burning activities emit large amounts of trace gases and aerosols on regional to global scales. We present a compilation of emission factors (EFs) and emission ratios from the eastern portion of the Fire Influence on Regional to Global Environments and Air Quality (FIREX‐AQ) campaign in 2019 in the United States, which sampled burning of crop residues and other prescribed fire fuels. FIREX‐AQ provided comprehensive chemical characterization of 53 crop residue and 22 prescribed fires. Crop residues burned at different modified combustion efficiencies (MCE), with corn residue burning at higher MCE than other fuel types. Prescribed fires burned at lower MCE (<0.90) which is typical, while grasslands burned at lower MCE (0.90) than normally observed due to moist, green, growing season fuels. Most non‐methane volatile organic compounds (NMVOCs) were significantly anticorrelated with MCE except for ethanol and NMVOCs that were measured with less certainty. We identified 23 species where crop residue fires differed by more than 50% from prescribed fires at the same MCE. Crop residue EFs were greater for species related to agricultural chemical use and fuel composition as well as oxygenated NMVOCs possibly due to the presence of metals such as potassium. Prescribed EFs were greater for monoterpenes (5×). FIREX‐AQ crop residue average EFs generally agreed with the previous agricultural fire study in the US but had large disagreements with global compilations. FIREX‐AQ observations show the importance of regionally‐specific and fuel‐specific EFs as first steps to reduce uncertainty in modeling the air quality impacts of fire emissions. Plain Language Summary: Crop residue and prescribed fires emit pollution that impacts air quality. FIREX‐AQ provided observations of these emissions to better characterize their variability with a detailed set of chemical observations. These observations showed significant differences in the emissions from burning different crops (corn, rice, soybean, wheat) compared to other prescribed fires or grasslands that may be due to differences in the fuel composition, the use of agricultural chemicals, and moisture levels. Overall, FIREX‐AQ observations for crop residue fires compared better with previous results in the region than with globally averaged information. The campaign observed even greater variability across EFs than previous studies, suggesting that new methods must be developed to take this into account to improve predictions of the air quality impacts of burning these fuels. Key Points: Corn residue burned at higher modified combustion efficiency than rice or soybean residueImpacts of fire emissions >6 hr downwind on OH reactivity will be more influenced by species that are less important at the sourceEmission factors from crop residue fires agreed better with previous results from the same region than with global compilations [ABSTRACT FROM AUTHOR]

The NOAA/NASA Fire Influence on Regional to Global Environments and Air Quality (FIREX‐AQ) experiment was a multi‐agency, inter‐disciplinary research effort to: (a) obtain detailed measurements of trace gas and aerosol emissions from wildfires and prescribed fires using aircraft, satellites and ground‐based instruments, (b) make extensive suborbital remote sensing measurements of fire dynamics, (c) assess local, regional, and global modeling of fires, and (d) strengthen connections to observables on the ground such as fuels and fuel consumption and satellite products such as burned area and fire radiative power. From Boise, ID western wildfires were studied with the NASA DC‐8 and two NOAA Twin Otter aircraft. The high‐altitude NASA ER‐2 was deployed from Palmdale, CA to observe some of these fires in conjunction with satellite overpasses and the other aircraft. Further research was conducted on three mobile laboratories and ground sites, and 17 different modeling forecast and analyses products for fire, fuels and air quality and climate implications. From Salina, KS the DC‐8 investigated 87 smaller fires in the Southeast with remote and in‐situ data collection. Sampling by all platforms was designed to measure emissions of trace gases and aerosols with multiple transects to capture the chemical transformation of these emissions and perform remote sensing observations of fire and smoke plumes under day and night conditions. The emissions were linked to fuels consumed and fire radiative power using orbital and suborbital remote sensing observations collected during overflights of the fires and smoke plumes and ground sampling of fuels. Plain Language Summary: The NOAA/NASA Fire Influence on Regional to Global Environments and Air Quality (FIREX‐AQ) experiment was aimed at understanding how fuel and fire conditions at the point of emission influence the chemistry of smoke, what conditions and processes control the rise of smoke plumes, what happens to smoke as it is distributed in the atmosphere, and how chemical transformation of smoke impacts air quality, weather, and climate downwind. Lessons learned from FIREX‐AQ will also be used to assess and improve the effectiveness of satellites for estimating the emissions from wildfires and prescribed burns and to reduce uncertainties associated with modeling and forecasting of smoke. Here we present an overview of the FIREX‐AQ effort, its motivation and design, with detailed descriptions of the measurements and analyses carried out, their connections to FIREX‐AQ science goals, and the early findings of this exceptionally broad effort to understand fire and its many impacts on the atmosphere. Key Points: This work is an overview of the NOAA/NASA Fire Influence on Regional to Global Environments and Air Quality (FIREX‐AQ) field experimentFIREX‐AQ investigated biomass burning emissions and chemistry and smoke transportFIREX‐AQ was a multi‐platform mission that also utilized fire modeling and satellite detections and validation [ABSTRACT FROM AUTHOR]

Wildfire emissions are a key contributor of carbonaceous aerosols and trace gases to the atmosphere. Induced by buoyant lifting, smoke plumes can be injected into the free troposphere and lower stratosphere, which by consequence significantly affects the magnitude and distance of their influences on air quality and radiation budget. However, the vertical allocation of emissions when smoke escapes the planetary boundary layer (PBL) and the mechanism modulating it remain unclear. We present an inverse modeling framework to estimate the wildfire emissions, with their temporal and vertical evolution being constrained by assimilating aerosol extinction profiles observed from the airborne Differential Absorption Lidar‐High Spectral Resolution Lidar during the Fire Influence on Regional to Global Environments and Air Quality field campaign. Three fire events in the western U.S., which exhibit free‐tropospheric injections are examined. The constrained smoke emissions indicate considerably larger fractions of smoke injected above the PBL (f>PBL, 80%–94%) versus the column total, compared to those estimated by the WRF‐Chem model using the default plume rise option (12%–52%). The updated emission profiles yield improvements for the simulated vertical structures of the downwind transported smoke, but limited refinement of regional smoke aerosol optical depth distributions due to the spatiotemporal coverage of flight observations. These results highlight the significance of improving vertical allocation of fire emissions on advancing the modeling and forecasting of the environmental impacts of smoke. Plain Language Summary: Wildfires play a key role in air quality and the climate system. Lofted smoke into the free troposphere has been found to extend over long distances imposing regional and global impacts, but the vertical allocation of smoke emissions associated with fire combustion heat release and plume rise has yet to be objectively characterized and understood. Merging models and observations for four cases with free‐tropospheric injections detected, we show significantly larger fractions of emissions above the planetary boundary layer versus the column totals than those specified by the plume rise parameterization. The enlarged injection fractions are confirmed independently with observations over the transported smoke plumes. Implications for future improvement of the plume rise model are discussed. Key Points: An inverse modeling framework is developed to estimate vertical wildfire emission allocation using airborne lidar observationsInversions for four fire cases with free‐troposphere injections suggest larger fractions of smoke injected above the planetary boundary layer than modeledConstrained emission profiles lead to improved vertical allocation of transported smoke but limited refinement of regional aerosol optical depth [ABSTRACT FROM AUTHOR]

This paper introduces a new learning algorithm for accurate, physically driven time series prediction. The fundamental assumption behind the method is that the phenomena follow Ordinary Differential Equations. We investigate the general case where the time series follows an ODE of degree m∈N $m\in \mathbb{N}$. The resulting method is a learning algorithm based on the finite differences between the values of time series. We present the application of the method in the field of geodesy for polar motion prediction, the main objective of the present paper. We show that in this application, the linear form of the method is sufficient and offers competitive predictive performance. We present a baseline solution, in which we use historical polar motion time series from 1976 to predict up to the year 2020. The prediction horizon in this case is short‐term (up to 10 days into the future). In addition, we compare the prediction accuracy in the short‐term horizon with the results of the best performing model in the first Earth Orientation Prediction Comparison Campaign. On average, a 53% improvement in prediction performance is achieved. In further analyses, we compare the prediction accuracy for both short‐term and long‐term against the results of state‐of‐the‐art methods, namely Multichannel Singular Spectrum Analysis, and a combination of Singular Spectrum Analysis and Copula sampling. We show that the proposed method in this paper can outperform the mentioned two methods in both short and long‐term horizons, with an average improvement of the prediction performance of 54% and 52%, respectively. Plain Language Summary: Physical phenomena are usually represented in mathematical terms by (ordinary) differential equations. Polar motion is an important physical parameter that describes the changes in the Earth's rotation axis with respect to the surface of the Earth. There is, however, a latency of up to several days by which the values are provided by operational data centers. Therefore, predicted values are used for applications such as spacecraft navigation that would need instantaneous information about polar motion. In order to predict polar motion time series, we introduce a new learning approach that is based on fitting ordinary differential equations to data. We specifically focus on second‐order differential equations, since they are typically more accurate and many physical phenomena follow an equation of this type. We demonstrate by extensive comparative analyses that our proposed method outperforms other methods both in short and long‐term predictions. Key Points: We introduce a new learning approach called neural ordinary differential equation differential learning for time series predictionWe present the application of the method for polar motion time series prediction, focusing on the linear form of the methodRigorous tests show that this method can achieve state‐of‐the‐art accuracy for both short and long‐term predictions [ABSTRACT FROM AUTHOR]

The presence of xenobiotic compounds in biological wastewater treatment processes with activated sludge may reduce microbial communities, disrupt microbial diversity, and diminish system performance. Shock loads and unusual operating events in these biological systems have negative impacts on their efficiency and reliability for pollutant degradation, thereby posing high risk to microorganisms and water quality of receiving treated water bodies. The severity and characteristics of the occurring damage are determined by the toxic contaminant's degree, nature and mode of application. This review highlights the effects of metabolic uncouplers, heavy metals, carbon nanotubes, pharmaceuticals and personal care products, nanoparticles, and phenolic compounds stress on microbial biomass in activated sludge systems. The synergistic, antagonistic, and shock load toxic effects of hybrid substances exposure in an activated sludge sequential batch reactor (SBR) system on organic and nutrient removal, system efficiency, and toxicants biodegradation are discussed. The findings can be used to provide a theoretical foundation and professional assistance for optimizing the shock impacts of these toxic substances on biological wastewater treatment systems, which will help to reduce their negative effects on treatment system efficiency. [ABSTRACT FROM AUTHOR]

Light absorption by phytoplankton drives marine primary production and determines ocean color. Phytoplankton absorption is a function of the pigment composition, wavelength, intracellular pigment concentration, and the cells' type. This paper presents phytoplankton absorption spectra reconstructed from in situ pigment concentration and a library of pigment‐specific absorption coefficients from 32 individual pigment standards, including chlorophylls, caretonoids and phycobilins. The samples dominated by small phytoplankton show no significant difference between calculated absorption and that measured by a spectrophotometer. The component of absorption due to large cells, determined by diagnostic pigments analysis, required correction for the package effect. For the global ocean, the reconstructed phytoplankton absorption was overestimated by 16% at 443 nm and underestimated by 13% over the range between 400 and 700 nm. Following our reconstruction protocol, this approach allows the estimation of phytoplankton absorption spectra from many locations where pigment concentration has been measured, but no directly observed phytoplankton absorption measurements are available. Plain Language Summary: Phytoplankton, the small plant life of aquatic systems, absorb light which, together with available nutrients, determines their biomass and ultimately the productivity of the ocean or water mass. In situ phytoplankton absorption measurements require specialized equipment and expertise and are therefore infrequent, limiting our understating of aquatic photosynthesis. Photosynthetic phytoplankton contain pigments which are easier to measure using standard equipment and analytical methods. As a result, in situ pigment concentrations are 10 times more common than phytoplankton absorption measurements and are obtained in a wide range of global environmental studies. In this study, we show how to reconstruct the phytoplankton light absorption measurement based on the measurements of pigment composition and concentration. The method was applied to a data set of 8,012 pigment samples that were collected from all regions of the world's oceans. We then generated a global map of reconstructed phytoplankton absorption which compares well to maps of phytoplankton absorption derived from satellite imagery. Our paper provides a tool which can be used globally by aquatic scientists to convert measurements of pigment concentration into absorption spectra. As a result, light absorption and the maximum photosynthesis by phytoplankton can be accurately estimated for more oceanic areas of the globe. Key Points: Observation of phytoplankton pigment concentration outnumber the in situ measurements of phytoplankton absorption spectraThis paper has developed a new technique for reconstructing phytoplankton absorption spectra from pigment concentrations aloneGlobal maps of reconstructed and remotely‐sensed absorption at the chlorophyll a maximum shows similar magnitude and spatial distribution [ABSTRACT FROM AUTHOR]

Biomass burning (BB) events are occurring globally with increasing frequency, and their emissions are having more impacts on human health and climate. Large ash particles are recognized as a BB product with major influences on soil and water environments. However, fine‐ash particles, which have diameters smaller than several microns and characteristic morphologies and compositions (mainly Ca and Mg carbonates), have not yet been explicitly considered as a major BB aerosol component either in field observations or climate models. This study measured BB aerosol samples using transmission electron microscopy (TEM) and ion chromatography during the Fire Influence on Regional to Global Environments and Air Quality (FIREX‐AQ) campaign. We show that significant amounts of fine ash‐bearing particles are transported >100 km from their fire sources. Our environmental chamber experiments suggest that they can act as cloud condensation and ice nuclei. We also found considerable amounts of fine ash‐bearing particles in the TEM samples collected during previous campaigns (Biomass Burning Observation Project and Megacity Initiative: Local and Global Research Observations). These ash particles are commonly mixed with organic matter and make up ∼8% and 5% of BB smoke by number and mass, respectively, in samples collected during the FIREX‐AQ campaign. The measured ash‐mass concentrations are approximately five times and six times greater than those of BB black carbon and potassium, respectively, scaling to an estimated global emission of 11.6 Tg yr−1 with a range of 8.8–16.3 Tg yr−1. Better characterization and constraints on these fine ash‐bearing particles will improve BB aerosol measurements and strengthen assessments of BB impacts on human health and climate. Plain Language Summary: Biomass burning (BB) events occur globally and impact climate and human health. Ash particles larger than ∼10 μm are well known as the main products of BB that contaminate the soil and water near the source regions. On the other hand, ash particles having diameters smaller than several microns, which are inhalable and can be transported long distances, are not yet recognized as a major aerosol component of BB smoke. This study reveals that such fine ash‐bearing particles are abundant in number (∼8%) and mass (∼5%) within BB smoke. The global emission of fine ash particles is estimated to be 11.6 Tg yr−1 with a range of 8.8–16.3 Tg yr−1. By considering their abundance and properties, we will strengthen assessments of BB impacts on human health and climate. Key Points: Ash‐bearing particles with diameters smaller than several microns can be one of the major aerosol components in biomass burning (BB) smokeFine ash‐bearing particles can act as cloud condensation or ice nuclei and influence cloud propertiesThe global emission of fine ash aerosol mass from BB is estimated to be ∼11.6 Tg yr−1 [ABSTRACT FROM AUTHOR]

Background & Aims: Bacterial translocation drives liver disease progression. We investigated whether functional genetic variants in toll‐like receptor 5 (TLR5), the receptor for bacterial flagellin, affect the risk for hepatocellular carcinoma (HCC). Methods: Healthy controls (n = 212), patients with alcohol abuse without liver disease (n = 382), and patients from a discovery cohort of alcohol‐associated cirrhosis (n = 372 including 79 HCC cases), a validation cohort of alcohol‐associated cirrhosis (n = 355 including 132 HCC cases), and a cohort of cirrhosis due to nonalcoholic steatohepatitis (NASH) (n = 145 including 62 HCC cases) were genotyped for the TLR5 rs5744174 and rs5744168 polymorphisms. Chemokine levels were measured by ELISA in patients' sera and supernatants of flagellin‐stimulated healthy monocytes. Results: Frequency of the TLR5 rs5744174 TT genotype was similar in healthy controls (33%), controls with alcohol abuse (34%), and patients with alcohol‐associated cirrhosis in the discovery (28%), validation (33%), and NASH cohort (31%). The TT genotype was enriched in patients with versus without HCC in the discovery, validation, and NASH cohort (41% vs 25%; 39% vs 29%; 40% vs 24%; p <.05 each). This genotype remained a risk factor for HCC (OR = 1.9; p =.01) after multivariate correction for age, gender, diabetes, and carriage of the PNPLA3 148M variant. Interleukin‐8 induction in monocytes from healthy controls and serum levels of interleukin‐8 and CXCL1 from cirrhotic patients with the TT genotype were significantly increased versus C allele carriers. Conclusion: The TLR5 rs5744174 polymorphism, affecting immune response to flagellin, is linked to occurrence of HCC in cirrhosis caused by steatohepatitis. [ABSTRACT FROM AUTHOR]

Cardiovascular disease has been the leading cause of death globally for the past 15 years. Following a major cardiac disease episode, the ideal treatment would be the replacement of the damaged tissue, due to the limited regenerative capacity of cardiac tissues. However, we suffer from a chronic organ donor shortage which causes approximately 20 people to die each day waiting to receive an organ. Bioprinting of tissues and organs can potentially alleviate this burden by fabricating low cost tissue and organ replacements for cardiac patients. Clinical adoption of bioprinting in cardiovascular medicine is currently limited by the lack of systematic demonstration of its effectiveness, high costs, and the complexity of the workflow. Here, we give a concise review of progress in cardiovascular bioprinting and its components. We further discuss the challenges and future prospects of cardiovascular bioprinting in clinical applications. [ABSTRACT FROM AUTHOR]

Introduction: Diagnosis of sarcoidosis is based on clinical status and radiologic specific findings. Tissue confirmation of noncaseating granulomas is crucial. Pathological confirmation of pulmonary sarcoidosis is most commonly accomplished by bronchoscopy, which has a diagnostic yield of approximately 60%–70%. Objectives: In this prospective study, we analysed potential benefit of EBUS‐TBNA and EBB combination, application of cell blocks and smears with puncturing more than one station of lymph nodes in order to determine optimal strategy in diagnosis of sarcoidosis. Methods: About 133 patients with suspicion of sarcoidosis (stage I and stage II) were included in this study. Each patient underwent conventional bronchoscopy with endobronchial biopsy (EBB) followed by the EBUS and puncturing at least two different lymph node stations. Results: Positive cytopathological verification of sarcoidosis in our study was obtained in 123 patients (92.5%). EBUS‐TBNA was diagnostic in 116 patients (87.2%). EBB was positive in 26 patients (19.55%). Combination of EBUS‐TBNA and EBB statistically increased diagnostic yield of sarcoidosis to 92.5%. Sensitivity of EBUS‐TBNA with EBB was 93.9%, specificity 100%, PPV 100% and NPV 20%. Conclusions: Combining EBUS‐TBNA from at least two lymph node stations and EBB increased diagnostic yield of sarcoidosis. Such diagnostic strategy had almost 93% of diagnostic yield in stage I and stage II of sarcoidosis. Taking into account the safety of the whole procedure with endobronchial ultrasonography combined with conventional endoscopy with EBB and its cost effectiveness, TBLB can be intended to diagnose stage III or IV of pulmonary sarcoidosis. [ABSTRACT FROM AUTHOR]

Wildfire smoke influences on air quality and atmospheric chemistry have been underscored by the increasing fire prevalence in recent years, and yet, the connection between fire, smoke emissions, and the subsequent transformation of this smoke in the atmosphere remains poorly constrained. Toward improving these linkages, we present a new method for coupling high time‐resolution satellite observations of fire radiative power with in situ observations of smoke aerosols and trace gases. We apply this technique to 13 fire plumes comprehensively characterized during the recent FIREX‐AQ mission and show that changes in fire radiative power directly translate into changes in conserved smoke tracers (CO2, CO, and black carbon aerosol) observed in the downwind smoke plume. The correlation is particularly strong for CO2 (mean r > 0.9). This method is important for untangling the competing effects of changing fire behavior versus the influence of dilution and atmospheric processing on the downwind evolution of measured smoke properties. Key Points: Geostationary satellite observations of fire radiative power are highly correlated with in situ airborne measurements of primary‐emission smoke tracersHigh‐resolution satellite observations are needed to disentangle how fire activity and plume dilution impact the downwind evolution of smokeDiurnal fire activity for wildfires observed during FIREX‐AQ is best parameterized using a bimodal Gaussian distribution to inform models [ABSTRACT FROM AUTHOR]

Background: Emerging data indicates that extracellular traps (ETs), structures formed by various immune cell types, may contribute to the pathology of noninfectious inflammatory diseases. Histone hypercitrullination is an important step in ETs formation and citrullinated histone H3 (H3cit) is considered a novel and specific biomarker of that process. In the present study we have evaluated circulating H3cit in stable asthmatics and investigated its relationship with asthma severity, pulmonary function and selected blood and bronchoalveolar lavage (BAL) biomarkers. Methods: In 60 white adult stable asthmatics and 50 well-matched controls we measured serum levels of H3cit. In asthmatics we also performed bronchoscopy with BAL. We analyzed blood and BAL biomarkers, including interleukin (IL)-4, IL-5, IL-6, IL-10, IL-12p70, IL-17A and interferon γ. For statistical analysis, Mann–Whitney U-test, χ2 test, one-way ANCOVA, ROC curve analysis and univariate linear regression were applied. Independent determinants of H3cit were established in a multiple linear regression model. Results: Asthma was characterized by elevated circulating H3cit (17.49 [11.25–22.58] vs. 13.66 [8.66–18.87] ng/ml, p = 0.03). In asthmatics positive associations were demonstrated between serum H3cit and lung function variables, including total lung capacity (TLC) (β = 0.37 [95% CI 0.24–0.50]) and residual volume (β = 0.38 [95% CI 0.25–0.51]). H3cit was increased in asthma patients receiving systemic steroids (p = 0.02), as well as in subjects with BAL eosinophilia above 144 cells/ml (p = 0.02). In asthmatics, but not in controls, circulating H3cit correlated well with number of neutrophils (β = 0.31 [95% CI 0.19–0.44]) and monocytes (β = 0.42 [95% CI 0.29–0.55]) in peripheral blood. Furthermore, BAL macrophages, BAL neutrophils, TLC, high-sensitivity C-reactive protein, Il-12p70 and bronchial obstruction degree were independent determinants of H3cit in a multivariate linear regression model. Conclusions: Asthma is characterized by increased circulating H3cit likely related to the enhanced lung ETs formation. Inhibition of ETs might be a therapeutic option in selected asthma phenotypes, such as neutrophilic asthma. [ABSTRACT FROM AUTHOR]

Background: Airway structural changes are important in asthma pathology and require further investigations. Objective: We sought to evaluate which computed tomography (CT) indices, bronchial histological traits, or blood and bronchoalveolar lavage (BAL) biomarkers correlate best with lung function abnormalities in asthma. Methods: In 105 white adult asthmatics (53 with a component of fixed airflow obstruction), we determined airway cross‐sectional geometry of two proximal (the right upper lobe apical segmental and the left apicoposterior) and two distal (the right and the left basal posterior) bronchi, quantified the low‐attenuation lung area (LAA%), and analysed clusters based on airway CT‐metrics. We also performed bronchofiberoscopy with BAL and endobronchial biopsy, assessed blood and BAL biomarkers, including interleukin (IL)‐4, IL‐5, IL‐6, IL‐10, IL‐12p70, IL‐17A, IL‐23, interferon (INF)γ and periostin, together with circulating a disintegrin and metalloproteinase domain‐containing protein (ADAM)33, and investigated interplays between analysed variables. Results: Patients with fixed airflow limitation were characterized by lower lumen area and increased wall area and wall thickness ratios in distal airways, accompanied by raised LAA%. They had also higher blood neutrophilia, blood and BAL eosinophilia, increased circulating fibrinogen, periostin, and ADAM33. Blood neutrophilia, serum high density lipoproteins, thyroid‐stimulating hormone, and shortened activated partial thromboplastin time were determinants of thicker reticular basement membrane (RBM). BAL eosinophilia was the only positive predictor of collagen I accumulation. Surprisingly, we observed a negative correlation between RBM thickening and collagen I deposit. Cluster analysis based on CT‐metrics of the right lower lobe basal posterior bronchus revealed three well‐separated clusters similar in age, asthma duration, and BMI, but different in RBM thickness, collagen I accumulation, and inflammatory markers. Conclusions and clinical relevance: Airway remodelling traits are mainly related to the Th2 profile, higher circulating ADAM33, and blood neutrophilia. Lung function abnormalities and RBM thickening correlate better with CT‐metrics of distal than proximal airways. [ABSTRACT FROM AUTHOR]

Wildfire is a natural and integral ecosystem process that is necessary to maintain species composition, structure, and ecosystem function. Extreme fires have been increasing over the last decades, which have a substantial impact on air quality, human health, the environment, and climate systems. Smoke aerosols can be transported over large distances, acting as pollutants that affect adjacent and distant downwind communities and environments. Fire emissions are a complicated mixture of trace gases and aerosols, many of which are short‐lived and chemically reactive, and this mixture affects atmospheric composition in complex ways that are not completely understood. We present a review of the current state of knowledge of smoke aerosol emissions originating from wildfires. Satellite observations, from both passive and active instruments, are critical to providing the ability to view the large‐scale influence of fire, smoke, and their impacts. Progress in the development of fire emission estimates to regional and global chemical transport models has advanced, although significant challenges remain, such as connecting ecosystems and fuels burned with dependent atmospheric chemistry. Knowledge of the impact of smoke on radiation, clouds, and precipitation has progressed and is an essential topical research area. However, current measurements and parameterizations are not adequate to describe the impacts on clouds of smoke particles (e.g., CNN, INP) from fire emissions in the range of representative environmental conditions necessary to advance science or modeling. We conclude by providing recommendations to the community that we believe will advance the science and understanding of the impact of fire smoke emissions on human and environmental health, as well as feedback with climate systems. Key Points: Modeling and satellite observations of smokeSmoke impacts on the atmospheric radiation and cloudsProvides recommendations for the future research [ABSTRACT FROM AUTHOR]

Particle scattering is an important process that determines both the light penetration through the water column and water‐leaving light. Backscattering, in combination with absorption, determines the remote‐sensing reflectance that is used in ocean color algorithms. Additionally, the wavelength dependence of the backscattering ratio can be related to the particle composition in seawater. Here, we examine the magnitude and the spectral behavior of the backscattering ratio against other bio‐optical properties based on a comprehensive set of continuous measurements collected in coastal waters of the Great Barrier Reef over 3 years. The site is located offshore of a major river, and close to the cross‐shelf transition of bottom sediments from terrigenous muds to marine carbonates. The backscattering ratio measured at 650 nm clearly clustered the data into two types. Type 1, which we identified as terrigenous mud with a low organic fraction, is characterized by backscattering ratio below 0.011 with a mean value of 0.005. Type 2, which we identified as marine carbonate with a higher organic fraction, has backscattering ratio above 0.011 with a mean value of 0.02. Within 3 years, study site was exposed to type 1‐dominated particles 13% of the time, and type 2 87%. The observed changes in the backscattering ratio at this one coastal site are as large as the variability seen throughout the global ocean. This work provides a better understanding of processes determining the optical characteristics and insights into optical parameterizations that can be used in process‐based optical modeling of the Great Barrier Reef. Key Points: The backscattering ratio at 650 nm measured at Lucinda Jetty Coastal Observatory clearly clustered the data into two typesDuring the study, the estimated exposure of the observatory to terrigenous mud and marine carbonate was 13% and 87%, respectivelyThe observed changes in the backscattering ratio at the study site are as large as the variability seen throughout the global ocean [ABSTRACT FROM AUTHOR]

Highlights from the article: Nanocrystalline metal oxide thin films act as a substrate or platform in many materials assembly strategies. FTIR spectra were obtained for MHDA-derivatized SnO SB 2 sb films, which had been soaked for 14-18 h in THF solutions of MHDA. As shown in Figure, the spectrum of MHDA-derivatized SnO SB 2 sb differs from the spectrum of free MHDA in a significant respect. To assess MHDA surface coverage, spectra from 10 MHDA-derivatized SnO SB 2 sb films where obtained.

The processes of alteration of airless bodies exposed to the space environment are referred to be as "space weathering." Multiple agents contribute generally to space weathering, to an extent that depends on the specific location of the surface within the solar system. Typical space weathering agents encountered in the solar system are solar radiation, solar wind and cosmic rays, magnetospheric plasma (e.g., at Jupiter or Saturn), and cosmic dust. The effect of space weathering is generally assessed by measuring the surfaces optical properties, for example, by near‐infrared spectroscopy. The alteration of the surfaces is due to a cumulative effect over time of all agents. We investigate in this paper the contribution of micrometeoroid (dust) bombardment on different asteroids, by using the Interplanetary Micrometeoroid Environment Model for the interplanetary dust populations and a simplified model of interstellar dust dynamics. We quantify, for different representative asteroids (main belt and Near Earth Objects [NEOs]), the particle cumulative flux, mass flux, impact velocity, and the kinetic impact energy deposited. This work is primarily intended to support laboratory work investigating the effect of energy deposition onto sample surfaces, as well as astronomical observations of optical properties of asteroid surfaces. Plain Language Summary: This paper explores the impact speed, mass flux, and kinetic energy flux of interplanetary and interstellar micrometeoroids on near‐earth and main belt asteroids from different dynamical families. This paper aims at providing constraints to current and future experimental work to understand the space weathering of asteroids surfaces induced by dust impacts.The simulations are performed using the latest version of the micrometeoroid environment model of the European Space Agency (Interplanetary Micrometeoroid Environment Model). We find that for all asteroids studied, the dust impactors deposit orders of magnitude less energy on the asteroid surfaces compared to the solar radiations but their contribution is comparable to that of heavy solar wind ions and electrons. We find that large cometary grains provide most of the mass flux delivered to asteroids surfaces, independently of the asteroids orbital elements, suggesting that dust bombardment is unlikely to play a significant role in driving the observed spectral trends among the different asteroids. While surface gardening by dust bombardment may, in turn, expose fresh material from the asteroid's surface, the required interplanetary dust population mass flux seems too low to efficiently counterbalance the surface weathering due to solar wind implantation. Key Points: This paper presents the modeling of micrometeoroid impacts on main belt and near-Earth asteroidsThe contribution of cometary, asteroidal, and interstellar dust as space weathering agents is evaluated [ABSTRACT FROM AUTHOR]

Abstract: Biomass burning produces smoke aerosols that are emitted into the atmosphere. Some smoke constituents, notably black carbon, are highly effective light‐absorbing aerosols (LAA). Emitted LAA can be transported to high‐albedo regions like the Greenland Ice Sheet (GrIS) and affect local snowmelt. In the summer, the effects of LAA in Greenland are uncertain. To explore how LAA affect GrIS snowmelt and surface energy flux in the summer, we conduct idealized global climate model simulations with perturbed aerosol amounts and properties in the GrIS snow and overlying atmosphere. The in‐snow and atmospheric aerosol burdens we select range from background values measured on the GrIS to unrealistically high values. This helps us explore the linearity of snowmelt response and to achieve high signal‐to‐noise ratios. With LAA operating only in the atmosphere, we find no significant change in snowmelt due to the competing effects of surface dimming and tropospheric warming. Regardless of atmospheric LAA presence, in‐snow black carbon‐equivalent mixing ratios greater than ~60 ng/g produce statistically significant snowmelt increases over much of the GrIS. We find that net surface energy flux changes correspond well to snowmelt changes for all cases. The dominant component of surface energy flux change is solar energy flux, but sensible and longwave energy fluxes respond to temperature changes. Atmospheric LAA dampen the magnitude of solar radiation absorbed by in‐snow LAA when both varieties are simulated. In general, the significant melt and surface energy flux changes we simulate occur with LAA quantities that have never been recorded in Greenland. [ABSTRACT FROM AUTHOR]

We present and discuss JTRF2014, the Terrestrial Reference Frame (TRF) the Jet Propulsion Laboratory constructed by combining space-geodetic inputs from very long baseline interferometry (VLBI), satellite laser ranging (SLR), Global Navigation Satellite Systems (GNSS), and Doppler orbitography and radiopositioning integrated by satellite submitted for the realization of ITRF2014. Determined through a Kalman filter and Rauch-Tung-Striebel smoother assimilating position observations, Earth orientation parameters, and local ties, JTRF2014 is a subsecular, time series-based TRF whose origin is at the quasi-instantaneous center of mass (CM) as sensed by SLR and whose scale is determined by the quasi-instantaneous VLBI and SLR scales. The dynamical evolution of the positions accounts for a secular motion term, annual, and semiannual periodic modes. Site-dependent variances based on the analysis of loading displacements induced by mass redistributions of terrestrial fluids have been used to control the extent of random walk adopted in the combination. With differences in the amplitude of the annual signal within the range 0.5-0.8 mm, JTRF2014-derived center of network-to-center of mass (CM-CN) is in remarkable agreement with the geocenter motion obtained via spectral inversion of GNSS, Gravity Recovery and Climate Experiment (GRACE) observations and modeled ocean bottom pressure from Estimating the Circulation and Climate of the Ocean (ECCO). Comparisons of JTRF2014 to ITRF2014 suggest high-level consistency with time derivatives of the Helmert transformation parameters connecting the two frames below 0.18 mm/yr and weighted root-mean-square differences of the polar motion (polar motion rate) in the order of 30 μas (17 μas/d). [ABSTRACT FROM AUTHOR]

Introduction Bronchofiberoscopy (BF) is a key tool used in the management of patients with respiratory diseases. Describing factors that contribute to patient anxiety surrounding BF has the potential to influence patient management and willingness to undergo the procedure again in the future if needed. Objectives This study sought to understand what factors influence anxiety and satisfaction experienced by patients undergoing BF under conscious sedation. Methods This study had a prospective observational character and evaluated 463 consecutive patients undergoing BF. Participants were divided into two groups, those undergoing BF for the first time and those who had undergone the procedure at least once in the past. Data were collected from three questionnaires prepared by the research team. Results Patients who had undergone multiple bronchoscopies were more satisfied with their physician's explanation of the procedure ( P < 0.0001), had a better understanding of the indications ( P < 0.0001) and potential complications ( P < 0.0001) of BF and knew what specific procedure was planned ( P < 0.001). Patients undergoing BF for the first time experienced anxiety before the procedure more frequently ( P < 0.001). No significant difference in satisfaction was observed between patient groups and 89% would agree to BF in the future. Conclusions Patients who had previously undergone BF were better prepared for their procedure; however, satisfaction levels after the procedure were similar in both groups. Results suggest that medical staff should target patients who have not undergone BF previously to relieve anxiety. [ABSTRACT FROM AUTHOR]

Black carbon (BC) concentrations observed in 22 snowpits sampled in the northwest sector of the Greenland ice sheet in April 2014 have allowed us to identify a strong and widespread BC aerosol deposition event, which was dated to have accumulated in the pits from two snow storms between 27 July and 2 August 2013. This event comprises a significant portion (57% on average across all pits) of total BC deposition over 10 months (July 2013 to April 2014). Here we link this deposition event to forest fires burning in Canada during summer 2013 using modeling and remote sensing tools. Aerosols were detected by both the Cloud-Aerosol Lidar with Orthogonal Polarization (on board CALIPSO) and Moderate Resolution Imaging Spectroradiometer (Aqua) instruments during transport between Canada and Greenland. We use high-resolution regional chemical transport modeling (WRF-Chem) combined with high-resolution fire emissions (FINNv1.5) to study aerosol emissions, transport, and deposition during this event. The model captures the timing of the BC deposition event and shows that fires in Canada were the main source of deposited BC. However, the model underpredicts BC deposition compared to measurements at all sites by a factor of 2-100. Underprediction of modeled BC deposition originates from uncertainties in fire emissions and model treatment of wet removal of aerosols. Improvements in model descriptions of precipitation scavenging and emissions from wildfires are needed to correctly predict deposition, which is critical for determining the climate impacts of aerosols that originate from fires. [ABSTRACT FROM AUTHOR]

Recent guidelines governing anti-diabetic medications increasingly advocate metformin as first-line therapy in all patients with type 2 diabetes. However, metformin could be associated with increased risk of acute kidney injury ( AKI), acute dialysis and lactate acidosis in marginal patients. In a retrospective nationwide cohort study, a total of 168 443 drug-naïve patients with type 2 diabetes ≥50 years, initiating treatment with either metformin or sulphonyl in Denmark between 2000 and 2012 were included in this study (70.7% initiated treatment with metformin); calculation of 1-year risk of acute dialysis was based on g-standardization of cause-specific Cox regression models for acute dialysis, end-stage renal disease and death. One-year risks of acute dialysis were 92.4 per 100 000 (95% CI, 67.1-121.3) and 142.7 per 100 000 (95% CI, 118.3-168.0) for sulphonylurea and metformin, respectively. The metformin-associated 1-year risk of acute dialysis was increased by 50.3 per 100 000 (95% CI, 7.9-88.6), corresponding to a risk ratio of 1.53 (95% CI, 1.06-2.23), and a number needed to harm of 1988, thus providing evidence of potential concerns pertaining to the increasing use of metformin. [ABSTRACT FROM AUTHOR]

The impact of transient carbon depletion on reproductive growth in Arabidopsis was investigated by transferring long-photoperiod-grown plants to continuous darkness and returning them to a light-dark cycle. After 2 days of darkness, carbon reserves were depleted in reproductive sinks, and RNA in situ hybridization of marker transcripts showed that carbon starvation responses had been initiated in the meristem, anthers and ovules. Dark treatments of 2 or more days resulted in a bare-segment phenotype on the floral stem, with 23-27 aborted siliques. These resulted from impaired growth of immature siliques and abortion of mature and immature flowers. Depolarization of PIN1 protein and increased DII-VENUS expression pointed to rapid collapse of auxin gradients in the meristem and inhibition of primordia initiation. After transfer back to a light-dark cycle, flowers appeared and formed viable siliques and seeds. A similar phenotype was seen after transfer to sub-compensation point irradiance or CO2. It also appeared in a milder form after a moderate decrease in irradiance and developed spontaneously in short photoperiods. We conclude that Arabidopsis inhibits primordia initiation and aborts flowers and very young siliques in C-limited conditions. This curtails demand, safeguarding meristem function and allowing renewal of reproductive growth when carbon becomes available again. [ABSTRACT FROM AUTHOR]

The farming practices in vineyards vary widely, but how does this affect vineyard soils? The main objective of this study was to evaluate the effects of vineyard management practices on soil organic matter and the soil microbial community. To this end, we investigated three adjacent vineyards in the Traisen valley, Austria, of which the soils had developed on the same parent material and under identical environmental/site conditions but were managed differently (esp. tillage, fertilizer application, cover crops) for more than 10 yrs. We found that topsoil bulk density ( BD) decreased with increasing tillage intensity, while subsoil BD showed the opposite trend. Soil organic carbon ( SOC) stocks in 0-50 cm depth increased from 10 kg m−2 in an unfertilized and frequently tilled vineyard to 17 kg m−2 in a regularly fertilized but less intensively tilled vineyard. Topsoil microbial biomass per unit SOC, estimated by the sum of microbial phospholipid fatty acids ( PLFAs), followed this trend, albeit not statistically significantly. Principal component analysis of PLFA patterns revealed that the microbial communities were compositionally distinct between different management practices. The fungal PLFA marker 18:2 ω6,9 was highest in the vineyard with the lowest amount of extractable Cu (by 0.01 m CaCl2), and the bacterial-to-fungal biomass ratio was positively correlated with extractable Cu. Our results indicate that tillage and fertilizer application of vineyards can strongly affect vineyard soil properties such as BD and SOC stocks and that the application of Cu-based fungicides may impair soil fungal communities. [ABSTRACT FROM AUTHOR]

We are witnessing an unprecedented period in which the urban and the regional, formerly quite distinct from one another, are blending together to define something new and different. Never before have regional approaches been more important in urban research, and urban emphases more influential in regional development theory and planning. As one of the only forums that explicitly combines the urban and the regional, IJURR has a rare opportunity to take the lead in defining and expanding critical urban/regional studies. This essay identifies eight challenging themes for innovative critical and comparative regional research. [ABSTRACT FROM AUTHOR]

Ozone may affect leaf photosynthesis even before visible symptoms become apparent. This study had the objective to test several parameters of chlorophyll fluorescence and leaf gas exchange for their usefulness as indicators of latent ozone injury in the field. Container-grown apple trees (Malus domestica Borkh. cv. Golden Delicious) were exposed to four different ozone levels in open-top chambers. Identical leaves were analyzed in fixed-time intervals for the characteristics of fast fluorescence induction kinetics in vivo. By using high-time resolution, characteristic parameters describing the early photochemical events could be calculated according to the JIP-test. Parameters responsive to the different ozone treatments showed clear dependence on the accumulated ozone dose. Ozone exposure immediately preceding the measurements was more important for the extent of the physiological effects than the total accumulated ozone dose. The most sensitive parameters were the turnover number N (indicating how many times QA has to be reduced for full reduction of all acceptors; positively correlated to ozone dose) and D0, the density of reaction centres per leaf area (negatively correlated to ozone dose). Most parameters analyzed showed clearer responses to ozone on the adaxial than on the abaxial surface of the leaf. Changes in the parameter N were better correlated to ozone doses with low cut-offs (AOT00 and AOT20), whereas changes in D0 and in the specific electron fluxes per reaction centre were mainly influenced by ozone doses with high cut-offs (AOT80 and AOT100). Leaf gas exchange analyses revealed a higher ozone sensitivity in carboxylation efficiency than in light utilization efficiency and in the rate of light-saturated net photosynthesis. All ozone-induced photosynthetic effects were observed in leaves showing no sign of visible leaf injury. This study identified fluorescence parameters that could be useful for rapid monitoring and early detection of latent leaf injury by ozone. [ABSTRACT FROM AUTHOR]

The article presents the changes in the physicochemical properties (temperature, conductivity and pH) of the water in the Ropa River in the Polish Carpathians that were caused by the Klimkówka Reservoir, which was commissioned for operation in 1994. The study uses data mainly from daily measurements of the physicochemical properties of the water in the Ropa River. The measurements were taken during the period prior to the reservoir being built (1982-1993) and from the period after the dam became operational (1994-2006). The measurements were conducted 16 km downriver from the dam. The conducted analysis shows that the Klimkówka Reservoir radically changed the thermal conditions and the annual cycle for water conductivity in the Ropa River. The pH of the water in the Ropa River experienced the smallest changes. [ABSTRACT FROM AUTHOR]

Proper work of grid-connected converters requires an accurate detection of phase angle, frequency and amplitude of grid voltage. Phase locked loops (PLLs) based on synchronous reference frame theory can be used for estimation of these grid parameters. One of the issues that could appear during estimation of grid parameters is appearance of DC offset in measured grid voltage. This DC component (offset) is usually entered in PLL structure via measurement and A/D conversion process. Undesirable induced DC offset could appear as part of the reference sine current of photovoltaic inverters or other grid-connected converters. A lot of standards define allowed PV inverter's DC current injection in the grid. In this study, we propose an improved PLL structure with capability to fully reject DC offset and noise which could appear in measured input grid voltage. The key component of the proposed PLL is two-phase generator with a closed control loop for DC offset and noise rejection. Obtained simulation and experimental results show that the proposed PLL structure can solve important issues of presence of noise and DC offset in measured grid voltage. The proposed PLL structure shows excellent dynamical performances in conditions of fast changes of grid parameters. [ABSTRACT FROM AUTHOR]

This study investigates (i) the effect of biochar amendments on soil microbial communities in temperate agricultural soils, (ii) the involvement of microorganisms ( MOs) in degradation of biochar and (iii) techniques to quantify degradation of biochar in short-term experiments. The study involved an incubation experiment and a pot experiment with two arable soils (a sandy acidic Planosol and a calcareous loamy Chernozem) amended with 13C-depleted biochar from wheat husk and willow plants. Phospholipid fatty acids ( PLFAs), 13C-PLFA, CO2, 13C-CO2, soil organic carbon ( Corg) and 13C-Corg were monitored for 100 days. Effects of biochar application on the soil microorganisms ( MOs) were generally minor. In the incubation experiment, microbial biomass was elevated by wheat husk biochar, especially in the Planosol. The increase in PLFAs was attributed to Gram-negative bacteria and actinomycetes. Fungi and Gram-positive bacteria were less affected. In the pot experiment, MOs did not respond to the addition of willow biochar. The effects of biochar were mainly attributed to an increase in the pH of the Planosol. Additionally, MOs were probably less responsive to inorganic fertilizer in biochar-amended soil. In the incubation, only the actinomycetal PLFA 10Me18:0 incorporated biochar C, while in the pot experiment, Gram-negative bacterial PLFAs (16:1ω7c, 16:1ω5c, 18:1ω7c) and Me16:0 & i17:1ω8 and i17:0 indicated degradation of biochar after 5 weeks. Uptake of around 20% biochar C in these PLFAs was monitored, which accounts for 2% biochar C in the total microbial biomass. From the PLFA data the mean residence time of biochar carbon was estimated in time-scales of centuries to millennia. The CO2 concentration decreased after biochar addition until its production was masked by root respiration. The use of 13C-CO2 labelling to estimate degradation was complicated by the interference with an initial negative priming effect and the dissolution/precipitation of carbonate. In conclusion, soil MOs were not particularly affected by addition of biochar, and the effects recorded were mainly attributed to changing environmental conditions after biochar addition. Nonetheless, uptake of 13C label into microbial PLFAs was successfully used to estimate microbial degradation of biochar in short-term experiments. [ABSTRACT FROM AUTHOR]

Biochar (BC) application as a soil amendment has aroused much interest and was found to considerably improve soil nutrient status and crop yields on poor, tropical soils. However, information on the effect of BC on temperate soils is still insufficient, with effects expected to differ from tropical soils. We investigated the effects of BC on soil nutrient dynamics, crop yield, and quality in a greenhouse pot experiment. We compared three agricultural soils (Planosol, Cambisol, Chernozem), and BCs of three different feedstocks (wheat straw [WS], mixed woodchips [WC], vineyard pruning [VP]) slowly pyrolyzed at 525°C, of which the latter was also pyrolyzed at 400°C. The BCs were applied at two rates (1% and 3%, which would correspond to 30 and 90 t ha-1 in the field). Three crops, namely mustard ( Sinapis alba L.), barley ( Hordeum vulgare L.), and red clover ( Trifolium pretense L.) were grown successively within one year. The investigated soil properties included pH, electrical conductivity (EC), cation-exchange capacity (CEC), calcium-acetate-lactate (CAL)-extractable P (PCAL) and K (KCAL), C, N, and nitrogen-supplying potential (NSP). The results show a pH increase in all soils. The CEC increased only on the Planosol. The C : N ratio increased at 3% application rate. Despite improving the soil nutrient status partly, yields of the first crop (mustard) and to a lesser extent of the second crop (barley) were significantly depressed through BC application (by up to 68%); the yield of clover as third crop was not affected. Only the BC from WS maintained yields in the range of the control and even increased barley yield by 6%. The initial yield reduction was accompanied by notable decreases (Cu, Fe, Mn, Zn) and increases (Mo) in micronutrient concentrations of plant tissues while nitrogen concentrations were hardly affected. The results of the pot experiment show that despite additional mineral fertilization, short-term growth inhibition may occur when applying BC without further treatment to temperate soils. [ABSTRACT FROM AUTHOR]

Abstract: The main goal of this study is to investigate similarities and differences between impediments to enterprise system (ES) implementation in transition and developed economies. The adopted research approach incorporates the system lifecycle and seeks to examine how the difficulties change across the lifecycle phases. The research builds on the opinions of ES practitioners from Poland, a transition economy from Central and Eastern Europe. The main findings illustrate that difficulties experienced by ES adoptions in developed economies seem to concentrate on the project phase where implementation activities are carried out whereas in Polish companies problems tend to span through all the lifecycle phases. The results also suggest that in transition economies practitioners have to deal mostly with system‐related issues across the whole lifecycle. In developed economies, on the contrary, the shift from system‐related to business‐related problems throughout the system lifecycle is observed. [ABSTRACT FROM AUTHOR]

Endocrine cells are evident at an early stage in bovine pancreatic development when the pancreas still consists of primitive epithelial cords. At this stage, the endocrine cells are interspersed between the precursor cells destined to form the ductulo-acinar trees of later exocrine lobules. We here demonstrate that, in bovine fetuses of crown rump length ≥ 11 cm, the endocrine cells become increasingly segregated from the developing exocrine pancreas by assembly into two units that differ in histogenesis, architecture, and fate. Small numbers of 'perilobular giant islets' are distinguishable from larger numbers of 'intralobular small islets'. The two types of islets arise in parallel from the ends of the ductal tree. Aside from differences in number, location, and size, the giant and small islets differ in cellular composition (predominantly insulin-synthesising cells vs. mixtures of endocrine cells), morphology (epithelial trabeculae with gyriform and rosette-like appearance vs. compact circular arrangements of endocrine cells), and in their relationships to intrapancreatic ganglia and nerves. A further difference becomes apparent during the antenatal period; while the 'interlobular small islets' persist in the pancreata of calves and adult cattle, the perilobular giant islets are subject to regression, characterised by involution of the parenchyma, extensive haemorrhage, leukocyte infiltration (myeloid and T-cells) and progressive fibrotic replacement. In conclusion, epithelial precursor cells of the ductolo-acinar tree may give rise to populations of pancreatic islets with different histomorphology, cellular composition and fates. This should be taken into account when using these cells for the generation of pancreatic islets for transplantation therapy. [ABSTRACT FROM AUTHOR]

The article presents a study which investigates the formation of toxic compounds that stay in the biochars after pyrolysis. It examines biochars from three feedstocks, such as spruce wood, poplar wood and wheat straw, that were pyrolyzed slowly. The study conducted X-ray diffractometry, Fourier-transform infrared spectroscopy (FTIR) and differential scanning calorimetry (DSC) to attain information on the mineralogical composition and molecular characteristics of biochars. The results of FTIR and DSC proposed a loss of labile, alipathic compounds during the formation of aromatic constituents and during pyrolysis.

Summary Background Churg-Strauss syndrome ( CSS) shares similarities with asthma and hypereosinophilic syndrome ( HES). Eicosanoids - important inflammatory and signaling molecules - are present in exhaled breath condensate ( EBC) and broncho-alveolar lavage fluid ( BALF). Objectives To assess eicosanoid profile both in EBC and BALF of CSS subjects searching for a pattern characteristic of this syndrome. Methods EBCs from 23 CSS patients, 30 asthmatics, 12 HES patients and 54 healthy controls ( HC) were assessed quantitatively for 19 eicosanoids by a high-performance liquid chromatography - tandem mass spectrometry ( HPLC- MS/ MS). In addition, in 21 of 23 CSS subjects and in nine asthmatics, eicosanoids were determined in BALF. Results EBC from CSS patients showed markedly elevated levels of 12- HETE as compared with other studied groups. BALF was characterized by a significant elevation of 12- HETE and its metabolite 12-tetranor HETE in CSS as compared with asthma. Clinical activity of CSS correlated with 12- HETE and its metabolites levels in BALF, but not in EBC. Conclusion and Clinical Relevance CSS is clearly distinguished from bronchial asthma, and HES by a marked increase in 12- HETE concentration in both EBC and BALF. This points to a possible new pathogenic mechanism in CSS and may help in future in establishing the diagnosis of CSS. [ABSTRACT FROM AUTHOR]