Gap-filling eddy covariance methane fluxes:Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET)

The positioning system of the ANTARES Neutrino Telescope

The ANTARES neutrino telescope, located 40km off the coast of Toulon in the Mediterranean Sea at a mooring depth of about 2475m, consists of twelve detection lines equipped typically with 25 storeys. Every storey carries three optical modules that detect Cherenkov light induced by charged secondary particles (typically muons) coming from neutrino interactions. As these lines are flexible structures fixed to the sea bed and held taut by a buoy, sea currents cause the lines to move and the storeys to rotate. The knowledge of the position of the optical modules with a precision better than 10cm is essential for a good reconstruction of particle tracks. In this paper the ANTARES positioning system is described. It consists of an acoustic positioning system, for distance triangulation, and a compass-tiltmeter system, for the measurement of the orientation and inclination of the storeys. Necessary corrections are discussed and the results of the detector alignment procedure are described

Risk profiles and one-year outcomes of patients with newly diagnosed atrial fibrillation in India: Insights from the GARFIELD-AF Registry.

BACKGROUND: The Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF) is an ongoing prospective noninterventional registry, which is providing important information on the baseline characteristics, treatment patterns, and 1-year outcomes in patients with newly diagnosed non-valvular atrial fibrillation (NVAF). This report describes data from Indian patients recruited in this registry. METHODS AND RESULTS: A total of 52,014 patients with newly diagnosed AF were enrolled globally; of these, 1388 patients were recruited from 26 sites within India (2012-2016). In India, the mean age was 65.8 years at diagnosis of NVAF. Hypertension was the most prevalent risk factor for AF, present in 68.5% of patients from India and in 76.3% of patients globally (P < 0.001). Diabetes and coronary artery disease (CAD) were prevalent in 36.2% and 28.1% of patients as compared with global prevalence of 22.2% and 21.6%, respectively (P < 0.001 for both). Antiplatelet therapy was the most common antithrombotic treatment in India. With increasing stroke risk, however, patients were more likely to receive oral anticoagulant therapy [mainly vitamin K antagonist (VKA)], but average international normalized ratio (INR) was lower among Indian patients [median INR value 1.6 (interquartile range {IQR}: 1.3-2.3) versus 2.3 (IQR 1.8-2.8) (P < 0.001)]. Compared with other countries, patients from India had markedly higher rates of all-cause mortality [7.68 per 100 person-years (95% confidence interval 6.32-9.35) vs 4.34 (4.16-4.53), P < 0.0001], while rates of stroke/systemic embolism and major bleeding were lower after 1 year of follow-up. CONCLUSION: Compared to previously published registries from India, the GARFIELD-AF registry describes clinical profiles and outcomes in Indian patients with AF of a different etiology. The registry data show that compared to the rest of the world, Indian AF patients are younger in age and have more diabetes and CAD. Patients with a higher stroke risk are more likely to receive anticoagulation therapy with VKA but are underdosed compared with the global average in the GARFIELD-AF. CLINICAL TRIAL REGISTRATION-URL: http://www.clinicaltrials.gov. Unique identifier: NCT01090362

Interplay of seasonal sunlight, air and leaf temperature in two alpine páramo species, Colombian Andes

In models of photosynthetic gas exchange, leaf temperature (Tleaf) is often calculated using a combination of derivations of the energy balance equation and measurements of air temperature (Tair). Yet, there are frequently large differences between Tleaf and Tair under natural field conditions, and an energy balance is complicated in complex canopies, especially in tropical ecosystems. In the present study, we aimed to quantify the variation in Tleaf relative to Tair under naturally varying PPFD values in two representative species (Espeletia grandiflora and Chusquea tessellata) of a tropical alpine ecosystem (páramo, Colombian Andes) during both wet and dry seasons. The results of a Structural Equation Model showed that Tleaf was strongly correlated with changes in Tair during both seasons, but large differences (up to 14 °C) occurred between Tair and Tleaf, during the dry season when PPFD varied the most, especially in C. tessellata. Thus, using Tair to proximate Tleaf under variable conditions of incident sunlight could generate substantial errors in estimates of temperature-sensitive physiological processes. These results should be valuable for evaluating the accuracy of carbon and water exchange models within current and future scenarios of climate change in tropical páramos, as well as other ecosystems. © 2018 Elsevier B.V

Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

© 2021 Elsevier B.V.Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET)

Opportunistic infections and AIDS malignancies early after initiating combination antiretroviral therapy in high-income countries

Background: There is little information on the incidence of AIDS-defining events which have been reported in the literature to be associated with immune reconstitution inflammatory syndrome (IRIS) after combined antiretroviral therapy (cART) initiation. These events include tuberculosis, mycobacterium avium complex (MAC), cytomegalovirus (CMV) retinitis, progressive multifocal leukoencephalopathy (PML), herpes simplex virus (HSV), Kaposi sarcoma, non-Hodgkin lymphoma (NHL), cryptococcosis and candidiasis. Methods: We identified individuals in the HIV-CAUSAL Collaboration, which includes data from six European countries and the US, who were HIV-positive between 1996 and 2013, antiretroviral therapy naive, aged at least 18 years, hadCD4+ cell count and HIV-RNA measurements and had been AIDS-free for at least 1 month between those measurements and the start of follow-up. For each AIDS-defining event, we estimated the hazard ratio for no cART versus less than 3 and at least 3 months since cART initiation, adjusting for time-varying CD4+ cell count and HIV-RNA via inverse probability weighting. Results: Out of 96 562 eligible individuals (78% men) with median (interquantile range) follow-up of 31 [13,65] months, 55 144 initiated cART. The number of cases varied between 898 for tuberculosis and 113 for PML. Compared with non-cART initiation, the hazard ratio (95% confidence intervals) up to 3 months after cART initiation were 1.21 (0.90-1.63) for tuberculosis, 2.61 (1.05-6.49) for MAC, 1.17 (0.34-4.08) for CMV retinitis, 1.18 (0.62-2.26) for PML, 1.21 (0.83-1.75) for HSV, 1.18 (0.87-1.58) for Kaposi sarcoma, 1.56 (0.82-2.95) for NHL, 1.11 (0.56-2.18) for cryptococcosis and 0.77 (0.40-1.49) for candidiasis. Conclusion: With the potential exception of mycobacterial infections, unmasking IRIS does not appear to be a common complication of cART initiation in high-income countries. © 2014 Wolters Kluwer Health | Lippincott Williams &amp; Wilkins