Measurements of reactive chlorocarbons over the Surinam tropical rain forest: indications for strong biogenic emissions

International audienceContrary to the understanding of the emissions and chemical behavior of halocarbons from anthropogenic sources (e.g. CFCs and HCFCs), the biogeochemistry of naturally emitted halocarbons is still poorly understood. We present measurements of chloromethane (methyl chloride, CH3Cl), trichloromethane (chloroform, CHCl3), dichloromethane (CH2Cl2), and tetrachloroethylene (C2Cl4) from air samples taken over the Surinam rainforest during the 1998 LBA/CLAIRE campaign. The samples were collected in stainless steel canisters on-board a Cessna Citation jet aircraft and analyzed in the laboratory using a gas chromatograph equipped with FID and ECD. The chlorocarbons we studied have atmospheric lifetimes of ~1 year or less, and appear to have significant emissions from natural sources including oceans, soils and vegetations, as well as biomass burning. These sources are primarily concentrated in the tropics (30º N-30º S). We detected an increase as a function of latitude of methyl chloride, chloroform, and tetrachloroethylene mixing ratios, in pristine air masses advected from the Atlantic Ocean toward the central Amazon. In the absence of significant biomass burning sources, we attribute this increase to biogenic emissions from the Surinam rainforest. From our measurements, we deduce fluxes from the Surinam rainforest of 7.6±1.8 ?g CH3Cl m?2 h?1, 1.11±0.08g CHCl3 ?m?2 h?1, and 0.36±0.07 ?g C2Cl4 m?2 h?1. Extrapolated to a global scale, our emission estimates suggest a large potential source of 2 Tg CH3Cl yr?1 from tropical forests, which could account for the net budget discrepancy (underestimation of sources), as indicated previously. In addition, our estimates suggest a potential emission of 57±17\,Gg C2C4 yr?1 from tropical forest soils, equal to half of the currently missing C2Cl4 sources. We hypothesize that the extensive deforestation over the last two decades relates to the observed global downward trend of atmospheric methyl chloride

Crossover from Anderson- to Kondo-like behavior: Universality induced by spin-charge separation

The thermodynamics of a lattice regularized asymmetric Anderson impurity in a correlated host is obtained by an exact solution. The crossover from the Anderson- to the Kondo-regime is studied, thus making contact with predictions by scaling theory. On the basis of the exact solution, the transition to universal Kondo behavior is shown to be realized by a graduate separation of the energy scales of spin and charge excitations.Comment: 18 pages, 5 figure

Atmospheric transport and chemistry of trace gases in LMDz5B: evaluation and implications for inverse modelling

Representation of atmospheric transport is a major source of error in the estimation of greenhouse gas sources and sinks by inverse modelling. Here we assess the impact on trace gas mole fractions of the new physical parameterizations recently implemented in the atmospheric global climate model LMDz to improve vertical diffusion, mesoscale mixing by thermal plumes in the planetary boundary layer (PBL), and deep convection in the troposphere. At the same time, the horizontal and vertical resolution of the model used in the inverse system has been increased. The aim of this paper is to evaluate the impact of these developments on the representation of trace gas transport and chemistry, and to anticipate the implications for inversions of greenhouse gas emissions using such an updated model. Comparison of a one-dimensional version of LMDz with large eddy simulations shows that the thermal scheme simulates shallow convective tracer transport in the PBL over land very efficiently, and much better than previous versions of the model. This result is confirmed in three-dimensional simulations, by a much improved reproduction of the radon-222 diurnal cycle. However, the enhanced dynamics of tracer concentrations induces a stronger sensitivity of the new LMDz configuration to external meteorological forcings. At larger scales, the inter-hemispheric exchange is slightly slower when using the new version of the model, bringing them closer to observations. The increase in the vertical resolution (from 19 to 39 layers) significantly improves the representation of stratosphere/troposphere exchange. Furthermore, changes in atmospheric thermodynamic variables, such as temperature, due to changes in the PBL mixing modify chemical reaction rates, which perturb chemical equilibriums of reactive trace gases. One implication of LMDz model developments for future inversions of greenhouse gas emissions is the ability of the updated system to assimilate a larger amount of high-frequency data sampled at high-variability stations. Others implications are discussed at the end of the paper

Mild hypothermia during cardiopulmonary bypass assisted CABG is associated with improved short- and long-term survival, a 18- year cohort study

Data substantiating the optimal patient body temperature during cooling procedures in cardiac operations are currently unavailable. To explore the optimal temperature strategy, we examined the association between temperature management and survival among patients during cardiopulmonary bypass assisted coronary artery bypass grafting (CABG) procedures on 30-days and 5-year postoperative survival. Adult patients (n = 5,672, 23.6% female and mean (SD) age of 66 (10) years) operated between 1997 and 2015 were included, with continuous measured intraoperative nasopharyngeal temperatures. The association between mortality and patient characteristics, laboratory parameters, the lowest intraoperative plateau temperature and intraoperative cooling/rewarming rates were examined by multivariate Cox regression analysis. Machine learning-based cluster analysis was used to identify patient subgroups based on pre-cooling parameters and explore whether specific subgroups benefitted from a particular temperature management. Mild hypothermia (32- 35°C) was independently associated with improved 30-days and 5-year survival compared to patients in other temperature categories regardless of operation year. 30 days and 5-year survival were 98% and 88% in the mild hypothermia group, whereas it amounted 93% and 80% in the severe hypothermia (<30°C). Normothermia (35-37°C) showed the lowest survival after 30 days and 5 years amounting 93% and 72%, respectively. Cluster analysis identified 8 distinct patient subgroups principally defined by gender, age, kidney function and weight. The full cohort and all patient subgroups displayed the highest survival at a temperature of 32°C. Given these associations, further prospective randomized controlled trials are needed to ascertain optimal patient temperatures during CPB

Brazilian spring wheat germplasm as source of genetic variability.

As part of a Canada-Brazil germplasm exchange, 106 modern and ancient Brazilian spring wheat cultivars have been genotyped and phenotypically evaluated in Canada since 201

Comparison of Machine Learning Models Including Preoperative, Intraoperative, and Postoperative Data and Mortality After Cardiac Surgery

Importance: A variety of perioperative risk factors are associated with postoperative mortality risk. However, the relative contribution of routinely collected intraoperative clinical parameters to short-term and long-term mortality remains understudied. Objective: To examine the performance of multiple machine learning models with data from different perioperative periods to predict 30-day, 1-year, and 5-year mortality and investigate factors that contribute to these predictions. Design, Setting, and Participants: In this prognostic study using prospectively collected data, risk prediction models were developed for short-term and long-term mortality after cardiac surgery. Included participants were adult patients undergoing a first-time valve operation, coronary artery bypass grafting, or a combination of both between 1997 and 2017 in a single center, the University Medical Centre Groningen in the Netherlands. Mortality data were obtained in November 2017. Data analysis took place between February 2020 and August 2021. Exposure: Cardiac surgery. Main Outcomes and Measures: Postoperative mortality rates at 30 days, 1 year, and 5 years were the primary outcomes. The area under the receiver operating characteristic curve (AUROC) was used to assess discrimination. The contribution of all preoperative, intraoperative hemodynamic and temperature, and postoperative factors to mortality was investigated using Shapley additive explanations (SHAP) values. Results: Data from 9415 patients who underwent cardiac surgery (median [IQR] age, 68 [60-74] years; 2554 [27.1%] women) were included. Overall mortality rates at 30 days, 1 year, and 5 years were 268 patients (2.8%), 420 patients (4.5%), and 612 patients (6.5%), respectively. Models including preoperative, intraoperative, and postoperative data achieved AUROC values of 0.82 (95% CI, 0.78-0.86), 0.81 (95% CI, 0.77-0.85), and 0.80 (95% CI, 0.75-0.84) for 30-day, 1-year, and 5-year mortality, respectively. Models including only postoperative data performed similarly (30 days: 0.78 [95% CI, 0.73-0.82]; 1 year: 0.79 [95% CI, 0.74-0.83]; 5 years: 0.77 [95% CI, 0.73-0.82]). However, models based on all perioperative data provided less clinically usable predictions, with lower detection rates; for example, postoperative models identified a high-risk group with a 2.8-fold increase in risk for 5-year mortality (4.1 [95% CI, 3.3-5.1]) vs an increase of 11.3 (95% CI, 6.8-18.7) for the high-risk group identified by the full perioperative model. Postoperative markers associated with metabolic dysfunction and decreased kidney function were the main factors contributing to mortality risk. Conclusions and Relevance: This study found that the addition of continuous intraoperative hemodynamic and temperature data to postoperative data was not associated with improved machine learning-based identification of patients at increased risk of short-term and long-term mortality after cardiac operations

Journal of clinical monitoring and computing 2016 end of year summary:monitoring cerebral oxygenation and autoregulation

In the perioperative and critical care setting, monitoring of cerebral oxygenation (ScO2) and cerebral autoregulation enjoy increasing popularity in recent years, particularly in patients undergoing cardiac surgery. Monitoring ScO2 is based on near infrared spectroscopy, and attempts to early detect cerebral hypoperfusion and thereby prevent cerebral dysfunction and postoperative neurologic complications. Autoregulation of cerebral blood flow provides a steady flow of blood towards the brain despite variations in mean arterial blood pressure (MAP) and cerebral perfusion pressure, and is effective in a MAP range between approximately 50-150 mmHg. This range of intact autoregulation may, however, vary considerably between individuals, and shifts to higher thresholds have been observed in elderly and hypertensive patients. As a consequence, intraoperative hypotension will be poorly tolerated, and might cause ischemic events and postoperative neurological complications. This article summarizes research investigating technologies for the assessment of ScO2 and cerebral autoregulation published in the Journal of Clinical Monitoring and Computing in 2016