BackgroundSepsis is an important cause of neonatal morbidity and mortality; therefore, the early diagnosis of neonatal sepsis is essential.MethodOur aim was to compare the diagnostic accuracy of procalcitonin (PCT), C-reactive protein (CRP), procalcitonin combined with C-reactive protein (PCT + CRP) and presepsin in the diagnosis of neonatal sepsis. We searched seven databases to identify studies that met the inclusion criteria. Two independent reviewers performed data extraction. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), area under curve (AUC), and corresponding 95% credible interval (95% CI) were calculated by true positive (TP), false positive (FP), false negative (FN), and true negative (TN) classification using a bivariate regression model in STATA 14.0 software. The pooled sensitivity, specificity, PLR, NLR, DOR, AUC, and corresponding 95% CI were the primary outcomes. Secondary outcomes included the sensitivity and specificity in multiple subgroup analyses.ResultsA total of 28 studies enrolling 2661 patients were included in our meta-analysis. The pooled sensitivity of CRP (0.71 (0.63, 0.78)) was weaker than that of PCT (0.85 (0.79, 0.89)), PCT + CRP (0.91 (0.84, 0.95)) and presepsin (0.94 (0.80, 0.99)) and the pooled NLR of presepsin (0.06 (0.02, 0.23)) and PCT + CRP (0.10 (0.05, 0.19)) were less than CRP (0.33 (0.26, 0.42)), and the AUC for presepsin (0.99 (0.98, 1.00)) was greater than PCT + CRP (0.96 (0.93, 0.97)), CRP (0.85 (0.82, 0.88)) and PCT (0.91 (0.89, 0.94)). The results of the subgroup analysis showed that 0.5–2 ng/mL may be the appropriate cutoff interval for PCT. A cut-off value > 10 mg/L for CRP had high sensitivity and specificity.ConclusionsThe combination of PCT and CRP or presepsin alone improves the accuracy of diagnosis of neonatal sepsis. However, further studies are required to confirm these findings.Electronic supplementary materialThe online version of this article (10.1186/s13054-018-2236-1) contains supplementary material, which is available to authorized users.

[1] Incident photosynthetically active radiation (PAR) is a key variable needed by almost all terrestrial ecosystem models. Unfortunately, the current incident PAR products estimated from remotely sensed data at spatial and temporal resolutions are not sufficient for carbon cycle modeling and various applications. In this study, the authors develop a new method based on the look-up table approach for estimating instantaneous incident PAR from the polar-orbiting Moderate Resolution Imaging Spectrometer (MODIS) data. Since the top-of-atmosphere (TOA) radiance depends on both surface reflectance and atmospheric properties that largely determine the incident PAR, our first step is to estimate surface reflectance. The approach assumes known aerosol properties for the observations with minimum blue reflectance from a temporal window of each pixel. Their inverted surface reflectance is then interpolated to determine the surface reflectance of other observations. The second step is to calculate PAR by matching the computed TOA reflectance from the look-up table with the TOA values of the satellite observations. Both the direct and diffuse PAR components, as well as the total shortwave radiation, are determined in exactly the same fashion. The calculation of a daily average PAR value from one or two instantaneous PAR values is also explored. Ground measurements from seven FLUXNET sites are used for validating the algorithm. The results indicate that this approach can produce reasonable PAR product at 1 km resolution and is suitable for global applications, although more quantitative validation activities are still needed.

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Incident photosynthetically active radiation (PAR) is an important parameter for terrestrial ecosystem models. Because of its high temporal resolution, the Geostationary Operational Environmental Satellite (GOES) observations are very suited to catch the diurnal variation of PAR. In this paper, a new method is developed to derive PAR using GOES data. What makes this new method distinct from the existing method is that it does not need external knowledge of atmospheric conditions. The new method retrieves both atmospheric and surface conditions using only at-sensor radiance through interpolation of time series of observations. Validations against ground measurement are carried out at four “FLUXNET” sites. The values of RMSE of estimated and ground-measured instantaneous PAR at the four sites are 130.71, 131.44, 141.16, and 190.22 μmol m−2 s−1, respectively. At the four validation sites, the RMSE as the percentage of estimated mean PAR value are 9.52%, 13.01%, 13.92%, and 24.09%, respectively; the biases are −101.54, 16.56, 11.09, and 53.64 μmol m−2 s−1, respectively. The independence of external atmospheric information enables this method to be applicable to many situations in which external atmospheric information is not available. In addition, topographic impacts on surface PAR are examined at the 1-km resolution at which PAR is retrieved using the GOES visible band data.

Anthropogenic CO 2 emission from fossil fuel combustion has major impacts on the global climate. The Orbiting Carbon Observatory 2 (OCO-2) observations have previously been used to estimate individual power plant emissions with a Gaussian plume model assuming constant wind fields. The present work assesses the feasibility of estimating power plant CO 2 emission using high resolution chemistry transport model simulations with OCO-2 observation data. In the new framework, 1.33 km Weather Research and Forecasting-Chem (WRF)-Chem simulation results are used to calculate the Jacobian matrix, which is then used with the OCO-2 XCO 2 data to obtain power plant daily mean emission rates, through a maximum likelihood estimation. We applied the framework to the seven OCO-2 observations of near mid-to-large coal burning power plants identified in Nassar et al (2017 Geophys. Res. Lett. 44, 10045-53). Our estimation results closely match the reported emission rates at the Westar power plant (Kansas, USA), with a reported value of 26.67 ktCO 2 /day, and our estimated value at 25.82-26.47 ktCO 2 /day using OCO-2 v8 data, and 22.09-22.80 ktCO 2 /day using v9 data. At Ghent, KY, USA, our estimations using three versions (v7, v8, and v9) range from 9.84-20.40 ktCO 2 /day, which are substantially lower than the reported value (29.17 ktCO 2 /day). We attribute this difference to diminished WRF-Chem wind field simulation accuracy. The results from the seven cases indicate that accurate estimation requires accurate meteorological simulations and high quality XCO 2 data. In addition, the strength and orientation (relative to the OCO-2 ground track) of the XCO 2 enhancement are important for accurate and reliable estimation. Compared with the Gaussian plume model based approach, the high resolution WRF-Chem simulation based approach provides a framework for addressing varying wind fields, and possible expansion to city level emission estimation.

Abstract. Regional atmospheric CO 2 inversions commonly use Lagrangian particle trajectory model simulations to calculate the required influence function, which quantifies the sensitivity of a receptor to flux sources. In this paper, an adjoint-based four-dimensional variational (4D-Var) assimilation system, WRF-CO2 4D-Var, is developed to provide an alternative approach. This system is developed based on the Weather Research and Forecasting (WRF) modeling system, including the system coupled to chemistry (WRF-Chem), with tangent linear and adjoint codes (WRFPLUS), and with data assimilation (WRFDA), all in version 3.6. In WRF-CO2 4D-Var, CO 2 is modeled as a tracer and its feedback to meteorology is ignored. This configuration allows most WRF physical parameterizations to be used in the assimilation system without incurring a large amount of code development. WRF-CO2 4D-Var solves for the optimized CO 2 flux scaling factors in a Bayesian framework. Two variational optimization schemes are implemented for the system: the first uses the limited memory Broyden-Fletcher-GoldfarbShanno (BFGS) minimization algorithm (L-BFGS-B) and the second uses the Lanczos conjugate gradient (CG) in an incremental approach. WRFPLUS forward, tangent linear, and adjoint models are modified to include the physical and dynamical processes involved in the atmospheric transport of CO 2 . The system is tested by simulations over a domain covering the continental United States at 48 km × 48 km grid spacing. The accuracy of the tangent linear and adjoint models is assessed by comparing against finite difference sensitivity. The system's effectiveness for CO 2 inverse modeling is tested using pseudo-observation data. The results of the sensitivity and inverse modeling tests demonstrate the potential usefulness of WRF-CO2 4D-Var for regional CO 2 inversions.

USGS national elevation dataset (NED) digital elevation model (DEM) data and four sets of light detection and ranging (LIDAR) DEMs were compared for Pitt County, North Carolina. The NED DEM has a spatial resolution of 30ϫ 30 m. Two sets of the LIDAR DEMs have a spatial resolution of 6.1ϫ 6.1 m and 15.2ϫ 15.2 m, respectively. To compare the DEMs spatially, two LIDAR DEMs were resampled into 30ϫ 30 m resolution. Statistically, the LIDAR DEMs were very similar to each other, and there was some difference with the LIDAR DEMs versus NED DEM. All five DEMs covering the floodplains between the cities of Greenville and Washington were then utilized to map a flood extent. The spatial patterns of individual categories on the maps agreed 87.4-95.0%. Finally, modeled inundation extents were examined against the 1999 flood event. The overall accuracy for selected flooded and nonflooded sites ranged 92.5-96.1%.