Northern peatlands are likely to be important in future carbon cycle‐climate feedbacks due to their large carbon pools and vulnerability to hydrological change. Use of non‐peatland‐specific models ...could lead to bias in modeling studies of peatland‐rich regions. Here, seven ecosystem models were used to simulate CO2fluxes at three wetland sites in Canada and the northern United States, including two nutrient‐rich fens and one nutrient‐poor,sphagnum‐dominated bog, over periods between 1999 and 2007. Models consistently overestimated mean annual gross ecosystem production (GEP) and ecosystem respiration (ER) at all three sites. Monthly flux residuals (simulated – observed) were correlated with measured water table for GEP and ER at the two fen sites, but were not consistently correlated with water table at the bog site. Models that inhibited soil respiration under saturated conditions had less mean bias than models that did not. Modeled diurnal cycles agreed well with eddy covariance measurements at fen sites, but overestimated fluxes at the bog site. Eddy covariance GEP and ER at fens were higher during dry periods than during wet periods, while models predicted either the opposite relationship or no significant difference. At the bog site, eddy covariance GEP did not depend on water table, while simulated GEP was higher during wet periods. Carbon cycle modeling in peatland‐rich regions could be improved by incorporating wetland‐specific hydrology and by inhibiting GEP and ER under saturated conditions. Bogs and fens likely require distinct plant and soil parameterizations in ecosystem models due to differences in nutrients, peat properties, and plant communities.
Key Points
Models overestimated photosynthesis and respiration at all sites
Simulated CO2 fluxes were more accurate at fen sites than at the bog site
CO2 flux residuals were positively correlated with water table height
Abstract
Background
Hand hygiene (HH) is an important patient safety measure linked to the prevention of health care-associated infection, yet how outbreaks affect HH performance has not been ...formally evaluated.
Methods
A controlled, interrupted time series was performed across 5 acute-care academic hospitals using group electronic monitoring. This system captures 100% of all hand sanitizer and soap dispenser activations via a wireless signal to a wireless hub; the number of activations is divided by a previously validated estimate of the number of daily HH opportunities per patient bed, multiplied by the hourly census of patients on the unit. Daily HH adherence 60 days prior and 90 days following outbreaks on inpatient units was compared to control units not in outbreaks over the same period, using a Poisson regression model adjusting for correlations within hospitals and units. Predictors of HH improvement were assessed in this multivariate model.
Results
In the 60 days prior to outbreaks, units destined for outbreaks had significantly lower HH adherence compared to control units (incidence rate ratio IRR, 0.91; 95% confidence interval CI, .90–.93; P < .0001). Following an outbreak, the HH adherence among the outbreak units increased above that of the controls (IRR, 1.04; 95% CI, 1.02–1.06; P < .0001). Greater improvements were noted for outbreaks on surgical units, for outbreaks involving antibiotic-resistant organisms and enteric pathogens, and in those outbreaks where health-care workers became ill.
Conclusions
Hospital outbreaks tend to occur in units with lower HH adherence and are associated with rapid improvements in HH performance. Group electronic monitoring of HH could be used to develop novel, prospective feedback interventions designed to avert hospital outbreaks.
The association between outbreaks and electronically monitored hand hygiene (HH) adherence was assessed in a multicenter, controlled, interrupted time series study. Hospital outbreaks occurred on units with lower HH adherence and were associated with rapid improvements in HH performance.
Proinflammatory caspases play an essential role in innate immune responses to infection by regulating the cleavage and activation of proinflammatory cytokines. Activation of these enzymes requires ...the assembly of an intracellular molecular platform, termed the inflammasome, which is comprised of members of the pyrin domain (PYD)-containing superfamily of apoptosis and inflammation-regulatory proteins. We report here the identification and characterization of a poxvirus-encoded PYD-containing protein that interacts with the ASC-1 component of the inflammasome and inhibits caspase-1 activation and the processing of IL-1β and IL-18 induced by diverse stimuli. Knockout viruses that do not express this protein are unable to productively infect monocytes and lymphocytes due to an abortive phenotype and are markedly attenuated in susceptible hosts due to decreased virus dissemination and enhanced inflammatory responses at sites of infection. Thus, modulation of inflammasome function constitutes an important immunomodulatory strategy employed by poxviruses to circumvent host antiviral responses.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Northern peatlands are likely to be important in future carbon cycle‐climate feedbacks due to their large carbon pools and vulnerability to hydrological change. Use of non‐peatland‐specific models ...could lead to bias in modeling studies of peatland‐rich regions. Here, seven ecosystem models were used to simulate CO
2
fluxes at three wetland sites in Canada and the northern United States, including two nutrient‐rich fens and one nutrient‐poor,
sphagnum
‐dominated bog, over periods between 1999 and 2007. Models consistently overestimated mean annual gross ecosystem production (GEP) and ecosystem respiration (ER) at all three sites. Monthly flux residuals (simulated – observed) were correlated with measured water table for GEP and ER at the two fen sites, but were not consistently correlated with water table at the bog site. Models that inhibited soil respiration under saturated conditions had less mean bias than models that did not. Modeled diurnal cycles agreed well with eddy covariance measurements at fen sites, but overestimated fluxes at the bog site. Eddy covariance GEP and ER at fens were higher during dry periods than during wet periods, while models predicted either the opposite relationship or no significant difference. At the bog site, eddy covariance GEP did not depend on water table, while simulated GEP was higher during wet periods. Carbon cycle modeling in peatland‐rich regions could be improved by incorporating wetland‐specific hydrology and by inhibiting GEP and ER under saturated conditions. Bogs and fens likely require distinct plant and soil parameterizations in ecosystem models due to differences in nutrients, peat properties, and plant communities.
Key Points
Models overestimated photosynthesis and respiration at all sites
Simulated CO2 fluxes were more accurate at fen sites than at the bog site
CO2 flux residuals were positively correlated with water table height
The Selecting Therapeutic Targets in Inflammatory Bowel Disease (STRIDE) initiative of the International Organization for the Study of Inflammatory Bowel Diseases (IOIBD) has proposed treatment ...targets in 2015 for adult patients with inflammatory bowel disease (IBD). We aimed to update the original STRIDE statements for incorporating treatment targets in both adult and pediatric IBD.
Based on a systematic review of the literature and iterative surveys of 89 IOIBD members, recommendations were drafted and modified in 2 surveys and 2 voting rounds. Consensus was reached if ≥75% of participants scored the recommendation as 7 to 10 on a 10-point rating scale.
In the systematic review, 11,278 manuscripts were screened, of which 435 were included. The first IOIBD survey identified the following targets as most important: clinical response and remission, endoscopic healing, and normalization of C-reactive protein/erythrocyte sedimentation rate and calprotectin. Fifteen recommendations were identified, of which 13 were endorsed. STRIDE-II confirmed STRIDE-I long-term targets of clinical remission and endoscopic healing and added absence of disability, restoration of quality of life, and normal growth in children. Symptomatic relief and normalization of serum and fecal markers have been determined as short-term targets. Transmural healing in Crohn’s disease and histological healing in ulcerative colitis are not formal targets but should be assessed as measures of the remission depth.
STRIDE-II encompasses evidence- and consensus-based recommendations for treat-to-target strategies in adults and children with IBD. This frameworkshould be adapted to individual patients and local resources to improve outcomes.
Improving predictive understanding of Earth system variability and change
requires data–model integration. Efficient data–model integration for
complex models requires surrogate modeling to reduce ...model evaluation time.
However, building a surrogate of a large-scale Earth system model (ESM) with
many output variables is computationally intensive because it involves a
large number of expensive ESM simulations. In this effort, we propose an
efficient surrogate method capable of using a few ESM runs to build an
accurate and fast-to-evaluate surrogate system of model outputs over large
spatial and temporal domains. We first use singular value decomposition to
reduce the output dimensions and then use Bayesian optimization techniques to
generate an accurate neural network surrogate model based on limited ESM
simulation samples. Our machine-learning-based surrogate methods can build
and evaluate a large surrogate system of many variables quickly. Thus,
whenever the quantities of interest change, such as a different objective
function, a new site, and a longer simulation time, we can simply extract the
information of interest from the surrogate system without rebuilding new
surrogates, which significantly reduces computational efforts. We apply the
proposed method to a regional ecosystem model to approximate the relationship
between eight model parameters and 42 660 carbon flux outputs. Results
indicate that using only 20 model simulations, we can build an accurate
surrogate system of the 42 660 variables, wherein the consistency between
the surrogate prediction and actual model simulation is 0.93 and the mean
squared error is 0.02. This highly accurate and fast-to-evaluate surrogate
system will greatly enhance the computational efficiency of data–model
integration to improve predictions and advance our understanding of the Earth
system.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Short- and long-term treatment targets in inflammatory bowel diseases (IBDs) evolved during the last decade, shifting from symptom control to endoscopic healing and patient-centered parameters. The ...STRIDE-II consensus placed these targets on a timeline from initiating treatment and introduced additional targets, normalization of serum and fecal biomarkers, restoration of quality of life, prevention of disability, and, in children, restoration of growth. Transmural healing in Crohn’s disease and histologic healing in ulcerative colitis currently serve as adjunct measures to gauge remission depth. However, whether early treatment according to a treat-to-target paradigm affects the natural course of IBD remains unclear, leading to the need for prospective disease-modification trials. The SPIRIT consensus defined the targets for these trials to assess the long-term impact of early treatment on quality of life, disability, disease complications, risk of neoplastic lesions, and mortality. As further data emerge about the risk-benefit balance of aiming toward deeper healing, the targets in treating IBDs may continue to shift.
Calibration of terrestrial ecosystem models is important but challenging. Bayesian inference implemented by Markov chain Monte Carlo (MCMC) sampling provides a comprehensive framework to estimate ...model parameters and associated uncertainties using their posterior distributions. The effectiveness and efficiency of the method strongly depend on the MCMC algorithm used. In this work, a differential evolution adaptive Metropolis (DREAM) algorithm is used to estimate posterior distributions of 21 parameters for the data assimilation linked ecosystem carbon (DALEC) model using 14 years of daily net ecosystem exchange data collected at the Harvard Forest Environmental Measurement Site eddy-flux tower. The calibration of DREAM results in a better model fit and predictive performance compared to the popular adaptive Metropolis (AM) scheme. Moreover, DREAM indicates that two parameters controlling autumn phenology have multiple modes in their posterior distributions while AM only identifies one mode. The application suggests that DREAM is very suitable to calibrate complex terrestrial ecosystem models, where the uncertain parameter size is usually large and existence of local optima is always a concern. In addition, this effort justifies the assumptions of the error model used in Bayesian calibration according to the residual analysis. The result indicates that a heteroscedastic, correlated, Gaussian error model is appropriate for the problem, and the consequent constructed likelihood function can alleviate the underestimation of parameter uncertainty that is usually caused by using uncorrelated error models.
The parameterization of key photosynthesis parameters is one of the key uncertain sources in modeling ecosystem gross primary productivity (GPP). Solar‐induced chlorophyll fluorescence (SIF) offers a ...good proxy for GPP since it marks the actual process of photosynthesis; while machine learning (ML) provides a robust approach to model the GPP‐SIF relationship. Here, we trained the boosted regressing tree (BRT) and the Random Forest ML models with Greenhouse Gases Observing Satellite SIF data and in situ GPP observations from 49 eddy covariance towers. These trained ML GPP‐SIF models were fed into the Energy Exascale Earth System Model (E3SM) Land Model (ELM) to generate ELM‐simulated global SIF estimates, which were then benchmarked against satellite SIF observations with a surrogate modeling approach. Our results indicated good modeling performance of the ML‐based GPP‐SIF relationship. The ELM model when fed with the ML GPP‐SIF models also can well predict the spatial‐temporal variations in SIF. We also found high model accuracy for the surrogate modeling. Model parameter sensitivity analysis suggested that the fraction of leaf nitrogen in RuBisCO (flnr) is the most sensitive parameter to the SIF; other sensitive parameters include the Ball‐Berry stomatal conductance slope (mbbopt) and the vcmax entropy (vcmaxse). The posterior uncertainty in simulated GPP was greatly reduced after benchmarking, and the model produced improved spatial patterns of mean GPP relative to FLUXCOM GPP. Our integrated approach provides a new avenue for improving land models and using remote‐sensing SIF, which can be further improved in the future with more ground‐ and satellite‐based observations.
Plain Language Summary
Model estimation of photosynthesis product, that is, gross primary productivity (GPP), is a challenging but vital task. One of the keys is to find better values for key parameters. This parameter searching process requires good proxies for GPP that can be widely available across space and time, good statistical methods to relate proxies to GPP and to make best estimations that reduce the gaps between modeled results and observations. Here, we designed a new method that use solar‐induced chlorophyll fluorescence (SIF, a good proxy for photosynthesis) as a key input, and employ machine learning (a robust way to relate SIF and GPP) and surrogate modeling (a good method for finding the best parameters), to improve the photosynthesis parameterization in the Energy Exascale Earth System Model (E3SM) Land Model (ELM), a state‐of‐the‐art terrestrial biosphere model. Our results demonstrate that this new integrated approach has great potential for improving the parameterization of key photosynthesis parameters in land models.
Key Points
We built a unique method to improve gross primary productivity (GPP) modeling in land models
This method integrates solar‐induced chlorophyll fluorescence observations, machine learning, and surrogate modeling
The method reduced posterior uncertainties in simulated GPP and improved the modeling of its spatial patterns
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DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, SIK, UILJ, UKNU, UL, UM, UPUK
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