Role of CC chemokine CCL6/C10 as a monocyte chemoattractant in a murine acute peritonitis.

The aim of this study was to determine the role of CC chemokine CCL6/C10 in acute inflammation. Intraperitoneal injection of thioglycollate increased peritoneal CCL6, which peaked at 4 h and remained elevated at 48 h. Neutralization of CCL6 significantly inhibited the macrophage infiltration (34-48% reduction), but not other cell types, without decreasing the other CC chemokines known to attract monocytes/macrophages. CCL6 was expressed in peripheral eosinophils and elicited macrophages, but not in elicited neutrophils. Peritoneal CCL6 level was not decreased in granulocyte-depleted mice where eosinophil influx was significantly impaired. Thus, CCL6 appears to contribute to the macrophage infiltration that is independent of other CC chemokines. Eosinophils pre-store CCL6, but do not release CCL6 in the peritoneum in this model of inflammation

Employing machine learning to predict adverse acute post-surgical outcomes following elective ulnar collateral ligament reconstruction

Background: Ulnar collateral ligament reconstruction ameliorates valgus elbow instability in various patient populations, including overhead athletes, patients with acute UCL rupture following high energy trauma, and those with chronic, subclinical elbow laxity. This study aims to explore machine learning algorithms to identify risk factors in patients undergoing elective UCL reconstruction in the ambulatory setting to predict postoperative outcomes. Methods: RStudio was used to create a filtering code to identify adult patients who underwent elective UCL reconstruction from 2008 to 2018 in the American college of surgeons national surgical quality improvement program database. Patients were analyzed using six ML algorithms, which were trained to predict outcomes such as extended length of stay, non-home discharge, and adverse events based on various patient characteristics and surgical variables. Algorithmic performance was then assessed and top performing algorithms underwent further analysis to determine relative feature importance using a permutation feature importance method. Results: ML exhibited excellent performance in predicting LOS, with an average AUC of 0.953, similar to that of logistic regression. Regarding NHD, ML demonstrated a 60.8% increase in AUC compared to LR. In predicting AAE, ML achieved an average AUC that was 12.7% higher than LR. Conclusions: The highly predictive capability of ML indicates the possibility to represent a procedure-specific complementary tool for the preoperative risk stratification process. This study provides a comprehensive analysis of UCL reconstruction in the management and outcomes of any patient, regardless of age or activity level

Interannual and spatial impacts of phenological transitions, growing season length, and spring and autumn temperatures on carbon sequestration: A North America flux data synthesis

Understanding feedbacks of ecosystem carbon sequestration to climate change is an urgent step in developing future ecosystem models. Using 187 site-years of flux data observed at 24 sites covering three plant functional types (i.e. evergreen forests (EF), deciduous forests (DF) and non-forest ecosystems (NF) (e.g., crop, grassland, wetland)) in North America, we present an analysis of both interannual and spatial relationships between annual net ecosystem production (NEP) and phenological indicators, including the flux-based carbon uptake period (CUP) and its transitions, degree-day-derived growing season length (GSL), and spring and autumn temperatures. Diverse responses were acquired between annul NEP and these indicators across PFTs. Forest ecosystems showed consistent patterns and sensitivities in the responses of annual NEP to CUP and its transitions both interannually and spatially. The NF ecosystems, on the contrary, exhibited different trends between interannual and spatial relationships. The impact of CUP onset on annual NEP in NF ecosystems was interannually negative but spatially positive. Generally, the GSL was observed to be a likely good indicator of annual NEP for all PFTs both interannually and spatially, although with relatively moderate correlations in NF sites. Both spring and autumn temperatures were positively correlated with annual NEP across sites while this potential was greatly reduced temporally with only negative impacts of autumn temperature on annual NEP in DF sites. Our analysis showed that DF ecosystems have the highest efficiency in accumulating NEP from warmer spring temperature and prolonged GSL, suggesting that future climate warming will favor deciduous species over evergreen species, and supporting the earlier observation that ecosystems with the greatest net carbon uptake have the longest GSL

Interannual and spatial impacts of phenological transitions, growing season length, and spring and autumn temperatures on carbon sequestration: A North America flux data synthesis

Understanding feedbacks of ecosystem carbon sequestration to climate change is an urgent step in developing future ecosystem models. Using 187 site-years of flux data observed at 24 sites covering three plant functional types (i.e. evergreen forests (EF), deciduous forests (DF) and non-forest ecosystems (NF) (e.g., crop, grassland, wetland)) in North America, we present an analysis of both interannual and spatial relationships between annual net ecosystem production (NEP) and phenological indicators, including the flux-based carbon uptake period (CUP) and its transitions, degree-day-derived growing season length (GSL), and spring and autumn temperatures. Diverse responses were acquired between annul NEP and these indicators across PFTs. Forest ecosystems showed consistent patterns and sensitivities in the responses of annual NEP to CUP and its transitions both interannually and spatially. The NF ecosystems, on the contrary, exhibited different trends between interannual and spatial relationships. The impact of CUP onset on annual NEP in NF ecosystems was interannually negative but spatially positive. Generally, the GSL was observed to be a likely good indicator of annual NEP for all PFTs both interannually and spatially, although with relatively moderate correlations in NF sites. Both spring and autumn temperatures were positively correlated with annual NEP across sites while this potential was greatly reduced temporally with only negative impacts of autumn temperature on annual NEP in DF sites. Our analysis showed that DF ecosystems have the highest efficiency in accumulating NEP from warmer spring temperature and prolonged GSL, suggesting that future climate warming will favor deciduous species over evergreen species, and supporting the earlier observation that ecosystems with the greatest net carbon uptake have the longest GSL

Controlling plasma properties under differing degrees of electronegativity using odd harmonic dual frequency excitation

International audienceThe charged particle dynamics in low-pressure oxygen plasmas excited by odd harmonic dual frequency waveforms (low frequency of 13.56 MHz and high frequency of 40.68 MHz) are investigated using a one-dimensional numerical simulation in regimes of both low and high electronegativity. In the low electronegativity regime, the time and space averaged electron and negative ion densities are approximately equal and plasma sustainment is dominated by ionisation at the sheath expansion for all combinations of low and high frequency and the phase shift between them. In the high electronegativity regime, the negative ion density is a factor of 15--20 greater than the low electronegativity cases. In these cases, plasma sustainment is dominated by ionisation inside the bulk plasma and at the collapsing sheath edge when the contribution of the high frequency to the overall voltage waveform is low. As the high frequency component contribution to the waveform increases, sheath expansion ionisation begins to dominate. It is found that the control of the average voltage drop across the plasma sheath and the average ion flux to the powered electrode are similar in both regimes of electronegativity, despite the differing electron dynamics using the considered dual frequency approach. This offers potential for similar control of ion dynamics under a range of process conditions, independent of the electronegativity. This is in contrast to ion control offered by electrically asymmetric waveforms where the relationship between the ion flux and ion bombardment energy is dependent upon the electronegativity

Enhanced control of the ionization rate in radio-frequency plasmas with structured electrodes via tailored voltage waveforms

International audienceRadio-frequency capacitively coupled plasmas that incorporate structured electrodes enable increases in the electron density within spatially localized regions through the hollow cathode effect (HCE). This enables enhanced control over the spatial profile of the plasma density, which is useful for several applications including materials processing, lighting and spacecraft propulsion. However, asymmetries in the powered and grounded electrode areas inherent to the hollow cathode geometry lead to the formation of a time averaged dc self-bias voltage at the powered electrode. This bias alters the energy and flux of secondary electrons leaving the surface of the cathode and consequentially can moderate the increased localized ionization afforded by the hollow cathode discharge. In this work, two-dimensional fluid-kinetic simulations are used to demonstrate control of the dc self-bias voltage in a dual-frequency driven (13.56, 27.12 MHz), hollow cathode enhanced, capacitively coupled argon plasma over the 66.6--200 Pa (0.5--1.5 Torr) pressure range. By varying the phase offset of the 27.12 MHz voltage waveform, the dc self-bias voltage varies by 10%--15% over an applied peak-to-peak voltage range of 600--1000 V, with lower voltages showing higher modulation. Resulting ionization rates due to secondary electrons within the hollow cathode cavity vary by a factor of 3 at constant voltage amplitude, demonstrating the ability to control plasma properties relevant for maintaining and enhancing the HCE

Particle growth in urban and industrial plumes in Texas

Particle size distributions and gas-phase particle precursors and tracer species were measured aboard an aircraft in the plumes downwind from industrial and urban sources in the vicinity of Houston, TX during the daytime in late August and early September 2000. Plumes originating from the Parish gas-fired and coal-fired power plant, petrochemical industries along the Houston ship channel, the petrochemical facilities near the Gulf coast, and the urban center of Houston were studied. Most of the particle mass flux advected downwind of Houston came from the industries and electrical utilities at the periphery of the city rather than from sources in the urban core. In SO2-rich plumes that did not contain elevated concentrations of volatile organic compounds (VOCs), particle volume increased with increasing plume oxidation (age) at a rate consistent with condensation and neutralization of the gas-phase oxidation products Of SO2. In plumes that were rich in both SO2 and VOCs, observed particle growth greatly exceeded that expected from SO2 oxidation, indicating the formation of organic particulate mass. In plumes that were enhanced in VOCs but not in SO2, and in the plume of the Houston urban center, no particle volume growth with increasing plume oxidation was detected. Since substantial particle volume growth was associated only with SO2-rich plumes, these results suggest that photochemical oxidation of SO2 is the key process regulating particle mass growth in all the studied plumes in this region. However, uptake of organic matter probably contributes substantially to particle mass in petrochemical plumes rich in both SO2 and VOCs. Quantitative studies of particle formation and growth in photochemical systems containing nitrogen oxides (NOx = NO + NO2 ), VOCs, and SO2 are recommended to extend those previously made in NOx-VOC systems

The role of thermal energy accommodation and atomic recombination probabilities in low pressure oxygen plasmas

International audienceSurface interaction probabilities are critical parameters that determine the behaviour of low pressure plasmas and so are crucial input parameters for plasma simulations that play a key role in determining their accuracy. However, these parameters are difficult to estimate without in situ measurements. In this work, the role of two prominent surface interaction probabilities, the atomic oxygen recombination coefficient ? O and the thermal energy accommodation coefficient ? E in determining the plasma properties of low pressure inductively coupled oxygen plasmas are investigated using two-dimensional fluid-kinetic simulations. These plasmas are the type used for semiconductor processing. It was found that ? E plays a crucial role in determining the neutral gas temperature and neutral gas density. Through this dependency, the value of ? E also determines a range of other plasma properties such as the atomic oxygen density, the plasma potential, the electron temperature, and ion bombardment energy and neutral-to-ion flux ratio at the wafer holder. The main role of ? O is in determining the atomic oxygen density and flux to the wafer holder along with the neutral-to-ion flux ratio. It was found that the plasma properties are most sensitive to each coefficient when the value of the coefficient is small causing the losses of atomic oxygen and thermal energy to be surface interaction limited rather than transport limited

Evaluating the agreement between measurements and models of net ecosystem exchange at different times and timescales using wavelet coherence: an example using data from the North American Carbon Program Site-Level Interim Synthesis

Earth system processes exhibit complex patterns across time, as do the models that seek to replicate these processes. Model output may or may not be significantly related to observations at different times and on different frequencies. Conventional model diagnostics provide an aggregate view of model–data agreement, but usually do not identify the time and frequency patterns of model–data disagreement, leaving unclear the steps required to improve model response to environmental drivers that vary on characteristic frequencies. Wavelet coherence can quantify the times and timescales at which two time series, for example time series of models and measurements, are significantly different. We applied wavelet coherence to interpret the predictions of 20 ecosystem models from the North American Carbon Program (NACP) Site-Level Interim Synthesis when confronted with eddy-covariance-measured net ecosystem exchange (NEE) from 10 ecosystems with multiple years of available data. Models were grouped into classes with similar approaches for incorporating phenology, the calculation of NEE, the inclusion of foliar nitrogen (N), and the use of model–data fusion. Models with prescribed, rather than prognostic, phenology often fit NEE observations better on annual to interannual timescales in grassland, wetland and agricultural ecosystems. Models that calculated NEE as net primary productivity (NPP) minus heterotrophic respiration (HR) rather than gross ecosystem productivity (GPP) minus ecosystem respiration (ER) fit better on annual timescales in grassland and wetland ecosystems, but models that calculated NEE as GPP minus ER were superior on monthly to seasonal timescales in two coniferous forests. Models that incorporated foliar nitrogen (N) data were successful at capturing NEE variability on interannual (multiple year) timescales at Howland Forest, Maine. The model that employed a model–data fusion approach often, but not always, resulted in improved fit to data, suggesting that improving model parameterization is important but not the only step for improving model performance. Combined with previous findings, our results suggest that the mechanisms driving daily and annual NEE variability tend to be correctly simulated, but the magnitude of these fluxes is often erroneous, suggesting that model parameterization must be improved. Few NACP models correctly predicted fluxes on seasonal and interannual timescales where spectral energy in NEE observations tends to be low, but where phenological events, multi-year oscillations in climatological drivers, and ecosystem succession are known to be important for determining ecosystem function. Mechanistic improvements to models must be made to replicate observed NEE variability on seasonal and interannual timescales

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives