Embracing fine-root system complexity in terrestrial ecosystem modeling.

Projecting the dynamics and functioning of the biosphere requires a holistic consideration of whole-ecosystem processes. However, biases toward leaf, canopy, and soil modeling since the 1970s have constantly left fine-root systems being rudimentarily treated. As accelerated empirical advances in the last two decades establish clearly functional differentiation conferred by the hierarchical structure of fine-root orders and associations with mycorrhizal fungi, a need emerges to embrace this complexity to bridge the data-model gap in still extremely uncertain models. Here, we propose a three-pool structure comprising transport and absorptive fine roots with mycorrhizal fungi (TAM) to model vertically resolved fine-root systems across organizational and spatial-temporal scales. Emerging from a conceptual shift away from arbitrary homogenization, TAM builds upon theoretical and empirical foundations as an effective and efficient approximation that balances realism and simplicity. A proof-of-concept demonstration of TAM in a big-leaf model both conservatively and radically shows robust impacts of differentiation within fine-root systems on simulating carbon cycling in temperate forests. Theoretical and quantitative support warrants exploiting its rich potentials across ecosystems and models to confront uncertainties and challenges for a predictive understanding of the biosphere. Echoing a broad trend of embracing ecological complexity in integrative ecosystem modeling, TAM may offer a consistent framework where modelers and empiricists can work together toward this grand goal.

Across-model spread and shrinking in predicting peatland carbon dynamics under global change.

Large across-model spread in simulating land carbon (C) dynamics has been ubiquitously demonstrated in model intercomparison projects (MIPs), and became a major impediment in advancing climate change prediction. Thus, it is imperative to identify underlying sources of the spread. Here, we used a novel matrix approach to analytically pin down the sources of across-model spread in transient peatland C dynamics in response to a factorial combination of two atmospheric CO2 levels and five temperature levels. We developed a matrix-based MIP by converting the C cycle module of eight land models (i.e., TEM, CENTURY4, DALEC2, TECO, FBDC, CASA, CLM4.5 and ORCHIDEE) into eight matrix models. While the model average of ecosystem C storage was comparable to the measurement, the simulation differed largely among models, mainly due to inter-model difference in baseline C residence time. Models generally overestimated net ecosystem production (NEP), with a large spread that was mainly attributed to inter-model difference in environmental scalar. Based on the sources of spreads identified, we sequentially standardized model parameters to shrink simulated ecosystem C storage and NEP to almost none. Models generally captured the observed negative response of NEP to warming, but differed largely in the magnitude of response, due to differences in baseline C residence time and temperature sensitivity of decomposition. While there was a lack of response of NEP to elevated CO2 (eCO2 ) concentrations in the measurements, simulated NEP responded positively to eCO2 concentrations in most models, due to the positive responses of simulated net primary production. Our study used one case study in Minnesota peatland to demonstrate that the sources of across-model spreads in simulating transient C dynamics can be precisely traced to model structures and parameters, regardless of their complexity, given the protocol that all the matrix models were driven by the same gross primary production and environmental variables.

Urban warming advances spring phenology but reduces the response of phenology to temperature in the conterminous United States.

Urbanization has caused environmental changes, such as urban heat islands (UHIs), that affect terrestrial ecosystems. However, how and to what extent urbanization affects plant phenology remains relatively unexplored. Here, we investigated the changes in the satellite-derived start of season (SOS) and the covariation between SOS and temperature (RT ) in 85 large cities across the conterminous United States for the period 2001-2014. We found that 1) the SOS came significantly earlier (6.1 ± 6.3 d) in 74 cities and RT was significantly weaker (0.03 ± 0.07) in 43 cities when compared with their surrounding rural areas (P < 0.05); 2) the decreased magnitude in RT mainly occurred in cities in relatively cold regions with an annual mean temperature <17.3 °C (e.g., Minnesota, Michigan, and Pennsylvania); and 3) the magnitude of urban-rural difference in both SOS and RT was primarily correlated with the intensity of UHI. Simulations of two phenology models further suggested that more and faster heat accumulation contributed to the earlier SOS, while a decrease in required chilling led to a decline in RT magnitude in urban areas. These findings provide observational evidence of a reduced covariation between temperature and SOS in major US cities, implying the response of spring phenology to warming conditions in nonurban environments may decline in the warming future.

Anthropogenically driven climate and landscape change effects on inland water carbon dynamics: What have we learned and where are we going?

Inland waters serve as important hydrological connections between the terrestrial landscape and oceans but are often overlooked in global carbon (C) budgets and Earth System Models. Terrestrially derived C entering inland waters from the watershed can be transported to oceans but over 83% is either buried in sediments or emitted to the atmosphere before reaching oceans. Anthropogenic pressures such as climate and landscape changes are altering the magnitude of these C fluxes in inland waters. Here, we synthesize the most recent estimates of C fluxes and the differential contributions across inland waterbody types (rivers, streams, lakes, reservoirs, and ponds), including recent measurements that incorporate improved sampling methods, small waterbodies, and dried areas. Across all inland waters, we report a global C emission estimate of 4.40 Pg C/year (95% confidence interval: 3.95-4.85 Pg C/year), representing a 13% increase from the most recent estimate. We also review the mechanisms by which the most globally widespread anthropogenically driven climate and landscape changes influence inland water C fluxes. The majority of these drivers are expected to influence terrestrial C inputs to inland waters due to alterations in terrestrial C quality and quantity, hydrological pathways, and biogeochemical processing. We recommend four research priorities for the future study of anthropogenic alterations to inland water C fluxes: (1) before-and-after measurements of C fluxes associated with climate change events and landscape changes, (2) better quantification of C input from land, (3) improved assessment of spatial coverage and contributions of small inland waterbodies to C fluxes, and (4) integration of dried and drawdown areas to global C flux estimates. Improved measurements of inland water C fluxes and quantification of uncertainty in these estimates will be vital to understanding both terrestrial C losses and the "moving target" of inland water C emissions in response to rapid and complex anthropogenic pressures.

Seasonal changes in GPP/SIF ratios and their climatic determinants across the Northern Hemisphere.

Satellite-derived sun-induced chlorophyll fluorescence (SIF) has been increasingly used for estimating gross primary production (GPP). However, the relationship between SIF and GPP has not been well defined, impeding the translation of satellite observed SIF to GPP. Previous studies have generally assumed a linear relationship between SIF and GPP at daily and longer time scales, but support for this assumption is lacking. Here, we used the GPP/SIF ratio to investigate seasonal variations in the relationship between SIF and GPP over the Northern Hemisphere (NH). Based on multiple SIF products and MODIS and FLUXCOM GPP data, we found strong seasonal hump-shaped patterns for the GPP/SIF ratio over northern latitudes, with higher values in the summer than in the spring or autumn. This hump-shaped GPP/SIF seasonal variation was confirmed by examining different SIF products and was evident for most vegetation types except evergreen broadleaf forests. The seasonal amplitude of the GPP/SIF ratio decreased from the boreal/arctic region to drylands and the tropics. For most of the NH, the lowest GPP/SIF values occurred in October or September, while the maximum GPP/SIF values were evident in June and July. The most pronounced seasonal amplitude of GPP/SIF occurred in intermediate temperature and precipitation ranges. GPP/SIF was positively related to temperature in the early and late parts of the growing season, but not during the peak growing months. These shifting relationships between temperature and GPP/SIF across different months appeared to play a key role in the seasonal dynamics of GPP/SIF. Several mechanisms may explain the patterns we observed, and future research encompassing a broad range of climate and vegetation settings is needed to improve our understanding of the spatial and temporal relationships between SIF and GPP. Nonetheless, the strong seasonal variation in GPP/SIF we identified highlights the importance of incorporating this behavior into SIF-based GPP estimations.

Moisture availability mediates the relationship between terrestrial gross primary production and solar-induced chlorophyll fluorescence: Insights from global-scale variations.

Effective use of solar-induced chlorophyll fluorescence (SIF) to estimate and monitor gross primary production (GPP) in terrestrial ecosystems requires a comprehensive understanding and quantification of the relationship between SIF and GPP. To date, this understanding is incomplete and somewhat controversial in the literature. Here we derived the GPP/SIF ratio from multiple data sources as a diagnostic metric to explore its global-scale patterns of spatial variation and potential climatic dependence. We found that the growing season GPP/SIF ratio varied substantially across global land surfaces, with the highest ratios consistently found in boreal regions. Spatial variation in GPP/SIF was strongly modulated by climate variables. The most striking pattern was a consistent decrease in GPP/SIF from cold-and-wet climates to hot-and-dry climates. We propose that the reduction in GPP/SIF with decreasing moisture availability may be related to stomatal responses to aridity. Furthermore, we show that GPP/SIF can be empirically modeled from climate variables using a machine learning (random forest) framework, which can improve the modeling of ecosystem production and quantify its uncertainty in global terrestrial biosphere models. Our results point to the need for targeted field and experimental studies to better understand the patterns observed and to improve the modeling of the relationship between SIF and GPP over broad scales.

Multi-hypothesis comparison of Farquhar and Collatz photosynthesis models reveals the unexpected influence of empirical assumptions at leaf and global scales.

Mechanistic photosynthesis models are at the heart of terrestrial biosphere models (TBMs) simulating the daily, monthly, annual and decadal rhythms of carbon assimilation (A). These models are founded on robust mathematical hypotheses that describe how A responds to changes in light and atmospheric CO2 concentration. Two predominant photosynthesis models are in common usage: Farquhar (FvCB) and Collatz (CBGB). However, a detailed quantitative comparison of these two models has never been undertaken. In this study, we unify the FvCB and CBGB models to a common parameter set and use novel multi-hypothesis methods (that account for both hypothesis and parameter variability) for process-level sensitivity analysis. These models represent three key biological processes: carboxylation, electron transport, triose phosphate use (TPU) and an additional model process: limiting-rate selection. Each of the four processes comprises 1-3 alternative hypotheses giving 12 possible individual models with a total of 14 parameters. To broaden inference, TBM simulations were run and novel, high-resolution photosynthesis measurements were made. We show that parameters associated with carboxylation are the most influential parameters but also reveal the surprising and marked dominance of the limiting-rate selection process (accounting for 57% of the variation in A vs. 22% for carboxylation). The limiting-rate selection assumption proposed by CBGB smooths the transition between limiting rates and always reduces A below the minimum of all potentially limiting rates, by up to 25%, effectively imposing a fourth limitation on A. Evaluation of the CBGB smoothing function in three TBMs demonstrated a reduction in global A by 4%-10%, equivalent to 50%-160% of current annual fossil fuel emissions. This analysis reveals a surprising and previously unquantified influence of a process that has been integral to many TBMs for decades, highlighting the value of multi-hypothesis methods.

Introduction of Group Electronic Monitoring of Hand Hygiene on Inpatient Units: A Multicenter Cluster Randomized Quality Improvement Study.

BACKGROUND: The current approach to measuring hand hygiene (HH) relies on human auditors who capture <1% of HH opportunities and rapidly become recognized by staff, resulting in inflation in performance. Group electronic monitoring is a validated method of measuring HH adherence, but data demonstrating the clinical impact of this technology are lacking. METHODS: A stepped-wedge cluster randomized quality improvement study was performed on 26 inpatient medical and surgical units across 5 acute care hospitals in Ontario, Canada. The intervention involved daily HH reporting as measured by group electronic monitoring to guide unit-led improvement strategies. The primary outcome was monthly HH adherence (percentage) between baseline and intervention. Secondary outcomes included transmission of antibiotic-resistant organisms such as methicillin-resistant Staphylococcus aureus (MRSA) and other healthcare-associated infections. RESULTS: After adjusting for the correlation within inpatient units and hospitals, there was a significant overall improvement in HH adherence associated with the intervention (incidence rate ratio [IRR], 1.73 [95% confidence interval {CI}, 1.47-1.99]; P < .0001). Monthly HH adherence relative to the intervention increased from 29% (1 395 450/4 544 144) to 37% (598 035/1 536 643) within 1 month, followed by consecutive incremental increases up to 53% (804 108/1 515 537) by 10 months (P < .0001). There was a trend toward reduced healthcare-associated transmission of MRSA (IRR, 0.74 [95% CI, .53-1.04]; P = .08). CONCLUSIONS: The introduction of a system for group electronic monitoring led to rapid, significant improvements in HH performance within a 2-year period. This method offers significant advantages over direct observation for measurement and improvement of HH.

Global vegetation biomass production efficiency constrained by models and observations.

Plants use only a fraction of their photosynthetically derived carbon for biomass production (BP). The biomass production efficiency (BPE), defined as the ratio of BP to photosynthesis, and its variation across and within vegetation types is poorly understood, which hinders our capacity to accurately estimate carbon turnover times and carbon sinks. Here, we present a new global estimation of BPE obtained by combining field measurements from 113 sites with 14 carbon cycle models. Our best estimate of global BPE is 0.41 ± 0.05, excluding cropland. The largest BPE is found in boreal forests (0.48 ± 0.06) and the lowest in tropical forests (0.40 ± 0.04). Carbon cycle models overestimate BPE, although models with carbon-nitrogen interactions tend to be more realistic. Using observation-based estimates of global photosynthesis, we quantify the global BP of non-cropland ecosystems of 41 ± 6 Pg C/year. This flux is less than net primary production as it does not contain carbon allocated to symbionts, used for exudates or volatile carbon compound emissions to the atmosphere. Our study reveals a positive bias of 24 ± 11% in the model-estimated BP (10 of 14 models). When correcting models for this bias while leaving modeled carbon turnover times unchanged, we found that the global ecosystem carbon storage change during the last century is decreased by 67% (or 58 Pg C).

Cryptic phenology in plants: Case studies, implications, and recommendations.

Plant phenology-the timing of cyclic or recurrent biological events in plants-offers insight into the ecology, evolution, and seasonality of plant-mediated ecosystem processes. Traditionally studied phenologies are readily apparent, such as flowering events, germination timing, and season-initiating budbreak. However, a broad range of phenologies that are fundamental to the ecology and evolution of plants, and to global biogeochemical cycles and climate change predictions, have been neglected because they are "cryptic"-that is, hidden from view (e.g., root production) or difficult to distinguish and interpret based on common measurements at typical scales of examination (e.g., leaf turnover in evergreen forests). We illustrate how capturing cryptic phenology can advance scientific understanding with two case studies: wood phenology in a deciduous forest of the northeastern USA and leaf phenology in tropical evergreen forests of Amazonia. Drawing on these case studies and other literature, we argue that conceptualizing and characterizing cryptic plant phenology is needed for understanding and accurate prediction at many scales from organisms to ecosystems. We recommend avenues of empirical and modeling research to accelerate discovery of cryptic phenological patterns, to understand their causes and consequences, and to represent these processes in terrestrial biosphere models.

Informing climate models with rapid chamber measurements of forest carbon uptake.

Models predicting ecosystem carbon dioxide (CO2 ) exchange under future climate change rely on relatively few real-world tests of their assumptions and outputs. Here, we demonstrate a rapid and cost-effective method to estimate CO2 exchange from intact vegetation patches under varying atmospheric CO2 concentrations. We find that net ecosystem CO2 uptake (NEE) in a boreal forest rose linearly by 4.7 ± 0.2% of the current ambient rate for every 10 ppm CO2 increase, with no detectable influence of foliar biomass, season, or nitrogen (N) fertilization. The lack of any clear short-term NEE response to fertilization in such an N-limited system is inconsistent with the instantaneous downregulation of photosynthesis formalized in many global models. Incorporating an alternative mechanism with considerable empirical support - diversion of excess carbon to storage compounds - into an existing earth system model brings the model output into closer agreement with our field measurements. A global simulation incorporating this modified model reduces a long-standing mismatch between the modeled and observed seasonal amplitude of atmospheric CO2 . Wider application of this chamber approach would provide critical data needed to further improve modeled projections of biosphere-atmosphere CO2 exchange in a changing climate.

Impact of Infectious Disease Consultation on Quality of Care, Mortality, and Length of Stay in Staphylococcus aureus Bacteremia: Results From a Large Multicenter Cohort Study.

BACKGROUND: We assessed the impact of infectious disease (ID) consultation on management and outcome in patients with Staphylococcus aureus bacteremia (SAB). METHODS: A retrospective cohort study examined consecutive SAB patients from 6 academic and community hospitals between 2007 and 2010. Quality measures of management including echocardiography, repeat blood culture, removal of infectious foci, and antibiotic therapy were compared between ID consultation (IDC) and no ID consultation (NIDC) groups. A competing risk model with propensity score adjustment was used to compare in-hospital mortality and time to discharge. RESULTS: Of 847 SAB patients, 506 (60%) patients received an ID consultation and 341 (40%) patients did not. Echocardiography was done for 371 (73%) IDC and 191 (56%) NIDC patients (P < .0001) in hospital. Blood cultures were repeated within 2-4 days of bacteremia in 207 (41%) IDC and 107 (31%) NIDC patients (P = .0058). The infectious foci removal rate was not statistically different between the 2 groups. For empiric therapy, 474 (94%) IDC and 297 (87%) NIDC patients received appropriate antibiotics (P = .0013). For patients who finished the planned course of antibiotics, 285 of 422 (68%) IDC and 141 of 262 (54%) NIDC patients received the appropriate duration of antibiotic therapy (P = .0004). In hospital, 204 (24%) patients died: 104 of 506 (21%) IDC and 100 of 341 (29%) NIDC patients. Matched by propensity score, ID consultation had a subdistribution hazard ratio of 0.72 (95% confidence interval [CI], .52-.99; P = .0451) for in-hospital mortality and 1.28 (95% CI, 1.06-1.56; P = .0109) for being discharged alive. CONCLUSIONS: ID consultation is associated with better adherence to quality measures, reduced in-hospital mortality, and earlier discharge in patients with SAB.

Comparative effectiveness of cefazolin versus cloxacillin as definitive antibiotic therapy for MSSA bacteraemia: results from a large multicentre cohort study.

OBJECTIVES: We compared the effectiveness of cefazolin versus cloxacillin in the treatment of MSSA bacteraemia in terms of mortality and relapse. METHODS: A retrospective cohort study examined consecutive patients with Staphylococcus aureus bacteraemia from six academic and community hospitals between 2007 and 2010. Patients with MSSA bacteraemia who received cefazolin or cloxacillin as the predominant definitive antibiotic therapy were included in the study. Ninety-day mortality was compared between the two groups matched by propensity scores. RESULTS: Of 354 patients included in the study, 105 (30%) received cefazolin and 249 (70%) received cloxacillin as the definitive antibiotic therapy. In 90 days, 96 (27%) patients died: 21/105 (20%) in the cefazolin group and 75/249 (30%) in the cloxacillin group. Within 90 days, 10 patients (3%) had a relapse of S. aureus infection: 6/105 (6%) in the cefazolin group and 4/249 (2%) in the cloxacillin group. All relapses in the cefazolin group were related to a deep-seated infection. Based on the estimated propensity score, 90 patients in the cefazolin group were matched with 90 patients in the cloxacillin group. In the propensity score-matched groups, cefazolin had an HR of 0.58 (95% CI 0.31-1.08, P = 0.0846) for 90 day mortality. CONCLUSIONS: There was no significant clinical difference between cefazolin and cloxacillin in the treatment of MSSA bacteraemia with respect to mortality. Cefazolin was associated with non-significantly more relapses compared with cloxacillin, especially in deep-seated S. aureus infections.

Global patterns and controls of soil organic carbon dynamics as simulated by multiple terrestrial biosphere models: Current status and future directions.

Soil is the largest organic carbon (C) pool of terrestrial ecosystems, and C loss from soil accounts for a large proportion of land-atmosphere C exchange. Therefore, a small change in soil organic C (SOC) can affect atmospheric carbon dioxide (CO2) concentration and climate change. In the past decades, a wide variety of studies have been conducted to quantify global SOC stocks and soil C exchange with the atmosphere through site measurements, inventories, and empirical/process-based modeling. However, these estimates are highly uncertain, and identifying major driving forces controlling soil C dynamics remains a key research challenge. This study has compiled century-long (1901-2010) estimates of SOC storage and heterotrophic respiration (Rh) from 10 terrestrial biosphere models (TBMs) in the Multi-scale Synthesis and Terrestrial Model Intercomparison Project and two observation-based data sets. The 10 TBM ensemble shows that global SOC estimate ranges from 425 to 2111 Pg C (1 Pg = 1015 g) with a median value of 1158 Pg C in 2010. The models estimate a broad range of Rh from 35 to 69 Pg C yr-1 with a median value of 51 Pg C yr-1 during 2001-2010. The largest uncertainty in SOC stocks exists in the 40-65°N latitude whereas the largest cross-model divergence in Rh are in the tropics. The modeled SOC change during 1901-2010 ranges from -70 Pg C to 86 Pg C, but in some models the SOC change has a different sign from the change of total C stock, implying very different contribution of vegetation and soil pools in determining the terrestrial C budget among models. The model ensemble-estimated mean residence time of SOC shows a reduction of 3.4 years over the past century, which accelerate C cycling through the land biosphere. All the models agreed that climate and land use changes decreased SOC stocks, while elevated atmospheric CO2 and nitrogen deposition over intact ecosystems increased SOC stocks-even though the responses varied significantly among models. Model representations of temperature and moisture sensitivity, nutrient limitation, and land use partially explain the divergent estimates of global SOC stocks and soil C fluxes in this study. In addition, a major source of systematic error in model estimations relates to nonmodeled SOC storage in wetlands and peatlands, as well as to old C storage in deep soil layers.

Usefulness of previous methicillin-resistant Staphylococcus aureus screening results in guiding empirical therapy for S aureus bacteremia.

BACKGROUND: Staphylococcus aureus bacteremia (SAB) is an important infection. Methicillin-resistant S aureus (MRSA) screening is performed on hospitalized patients for infection control purposes. OBJECTIVE: To assess the usefulness of past MRSA screening for guiding empirical antibiotic therapy for SAB. METHODS: A retrospective cohort study examined consecutive patients with confirmed SAB and previous MRSA screening swab from six academic and community hospitals between 2007 and 2010. Diagnostic test properties were calculated for MRSA screening swab for predicting methicillin resistance of SAB. RESULTS: A total of 799 patients underwent MRSA screening swabs before SAB. Of the 799 patients, 95 (12%) had a positive and 704 (88%) had a negative previous MRSA screening swab. There were 150 (19%) patients with MRSA bacteremia. Overall, previous MRSA screening swabs had a positive likelihood ratio of 33 (95% CI 18 to 60) and a negative likelihood ratio of 0.45 (95% CI 0.37 to 0.54). Diagnostic accuracy differed depending on mode of acquisition (ie, community-acquired, nosocomial or health care-associated infection) (P<0.0001) and hospital (P=0.0002). At best, for health care-associated infection, prior MRSA screening swab had a positive likelihood ratio of 16 (95% CI 9 to 28) and a negative likelihood ratio of 0.27 (95% CI 0.17 to 0.41). CONCLUSIONS: A negative prior MRSA screening swab cannot reliably rule out MRSA bacteremia and should not be used to guide empirical antibiotic therapy for SAB. A positive prior MRSA screening swab greatly increases likelihood of MRSA, necessitating MRSA coverage in empirical antibiotic therapy for SAB.

HISTORIQUE: La bactériémie à Staphylococcus aureus (BSA) est une infection grave. Les patients hospitalisés subissent un dépistage du S. aureus résistant à la méthicilline (SARM) afin de prévenir les infections. OBJECTIF: Évaluer l'utilité d'un dépistage antérieur du SARM pour orienter l'antibiothérapie empirique de la BSA. MÉTHODOLOGIE: Les chercheurs ont effectué une étude de cohorte rétrospective dans six hôpitaux universitaires et hôpitaux généraux entre 2007 et 2010 auprès de patients consécutifs atteints d'une BSA confirmée ayant déjà subi un prélèvement de dépistage du SARM. Ils ont calculé les propriétés des tests diagnostiques par prélèvement pour diagnostiquer le SARM et prédire la résistance de la BSA à la méthicilline. RÉSULTATS: Au total, 799 patients avaient déjà subi des prélèvements pour dépister le SARM avant une BSA. De ce nombre, 95 (12 %) ont présenté un résultat positif et 704 (88 %) avaient déjà subi un prélèvement pour dépister le SARM. Cent cinquante patients (19 %) avaient une bactériémie à SARM. Dans l'ensemble, les prélèvements antérieurs pour dépister le SARM avaient un ratio de probabilité positif de 33 (95 % IC 18 à 60) et négatif de 0,45 (95 % IC 0,37 à 0,54). La précision diagnostique différait en fonction du mode d'acquisition (origine non nosocomiale, origine nosocomiale ou association aux soins de santé) (P<0,0001) et de l'hôpital (P=0,0002). Dans le meilleur des cas, en présence d'une infection associée aux soins de santé, un prélèvement antérieur pour dépister un SARM s'associait à un ratio de probabilité positif de 16 (95 % IC 9 à 28) et négatif de 0,27 (95 % IC 0,17 à 0,41). CONCLUSIONS: Un prélèvement antérieur négatif au SARM ne permet pas d'écarter une bactériémie par le SARM avec fiabilité et ne devrait pas orienter l'antibiothérapie empirique de la BSA. Un prélèvement antérieur positif au SARM accroît considérablement la probabilité de SARM, ce qui oblige à en tenir compte pour l'antibiothérapie empirique de la BSA.

Thermal, water, and land cover factors led to contrasting urban and rural vegetation resilience to extreme hot months.

With continuing global warming and urbanization, it is increasingly important to understand the resilience of urban vegetation to extreme high temperatures, but few studies have examined urban vegetation at large scale or both concurrent and delayed responses. In this study, we performed an urban-rural comparison using the Enhanced Vegetation Index and months that exceed the historical 90th percentile in mean temperature (referred to as "hot months") across 85 major cities in the contiguous United States. We found that hot months initially enhanced vegetation greenness but could cause a decline afterwards, especially for persistent (≥4 months) and intense (≥+2 °C) episodes in summer. The urban responses were more positive than rural in the western United States or in winter, but more negative during spring-autumn in the eastern United States. The east-west difference can be attributed to the higher optimal growth temperatures and lower water stress levels of the western urban vegetation than the rural. The urban responses also had smaller magnitudes than the rural responses, especially in deciduous forest biomes, and least in evergreen forest biomes. Within each biome, analysis at 1 km pixel level showed that impervious fraction and vegetation cover, local urban heat island intensity, and water stress were the key drivers of urban-rural differences. These findings advance our understanding of how prolonged exposure to warm extremes, particularly within urban environments, affects vegetation greenness and vitality. Urban planners and ecosystem managers should prioritize the long and intense events and the key drivers in fostering urban vegetation resilience to heat waves.

Utility of prior screening for methicillin-resistant Staphylococcus aureus in predicting resistance of S. aureus infections.

BACKGROUND: Screening for methicillin-resistant Staphylococcus aureus (MRSA) is intended to reduce nosocomial spread by identifying patients colonized by MRSA. Given the widespread use of this screening, we evaluated its potential clinical utility in predicting the resistance of clinical isolates of S. aureus. METHODS: We conducted a 2-year retrospective cohort study that included patients with documented clinical infection with S. aureus and prior screening for MRSA. We determined test characteristics, including sensitivity and specificity, of screening for predicting the resistance of subsequent S. aureus isolates. RESULTS: Of 510 patients included in the study, 53 (10%) had positive results from MRSA screening, and 79 (15%) of infecting isolates were resistant to methicillin. Screening for MRSA predicted methicillin resistance of the infecting isolate with 99% (95% confidence interval [CI] 98%-100%) specificity and 63% (95% CI 52%-74%) sensitivity. When screening swabs were obtained within 48 hours before isolate collection, sensitivity increased to 91% (95% CI 71%-99%) and specificity was 100% (95% CI 97%-100%), yielding a negative likelihood ratio of 0.09 (95% CI 0.01-0.3) and a negative predictive value of 98% (95% CI 95%-100%). The time between swab and isolate collection was a significant predictor of concordance of methicillin resistance in swabs and isolates (odds ratio 6.6, 95% CI 1.6-28.2). INTERPRETATION: A positive result from MRSA screening predicted methicillin resistance in a culture-positive clinical infection with S. aureus. Negative results on MRSA screening were most useful for excluding methicillin resistance of a subsequent infection with S. aureus when the screening swab was obtained within 48 hours before collection of the clinical isolate.

Quantification of human contribution to soil moisture-based terrestrial aridity.

Current knowledge of the spatiotemporal patterns of changes in soil moisture-based terrestrial aridity has considerable uncertainty. Using Standardized Soil Moisture Index (SSI) calculated from multi-source merged data sets, we find widespread drying in the global midlatitudes, and wetting in the northern subtropics and in spring between 45°N-65°N, during 1971-2016. Formal detection and attribution analysis shows that human forcings, especially greenhouse gases, contribute significantly to the changes in 0-10 cm SSI during August-November, and 0-100 cm during September-April. We further develop and apply an emergent constraint method on the future SSI's signal-to-noise (S/N) ratios and trends under the Shared Socioeconomic Pathway 5-8.5. The results show continued significant presence of human forcings and more rapid drying in 0-10 cm than 0-100 cm. Our findings highlight the predominant human contributions to spatiotemporally heterogenous terrestrial aridification, providing a basis for drought and flood risk management.

Validation and implementation of group electronic hand hygiene monitoring across twenty-four critical care units.

OBJECTIVES: An accurate estimate of the average number of hand hygiene opportunities per patient hour (HHO rate) is required to implement group electronic hand hygiene monitoring systems (GEHHMSs). We sought to identify predictors of HHOs to validate and implement a GEHHMS across a network of critical care units. DESIGN: Multicenter, observational study (10 hospitals) followed by quality improvement intervention involving 24 critical care units across 12 hospitals in Ontario, Canada. METHODS: Critical care patient beds were randomized to receive 1 hour of continuous direct observation to determine the HHO rate. A Poisson regression model determined unit-level predictors of HHOs. Estimates of average HHO rates across different types of critical care units were derived and used to implement and evaluate use of GEHHMS. RESULTS: During 2,812 hours of observation, we identified 25,417 HHOs. There was significant variability in HHO rate across critical care units. Time of day, day of the week, unit acuity, patient acuity, patient population and use of transmission-based precautions were significantly associated with HHO rate. Using unit-specific estimates of average HHO rate, aggregate HH adherence was 30.0% (1,084,329 of 3,614,908) at baseline with GEHHMS and improved to 38.5% (740,660 of 1,921,656) within 2 months of continuous feedback to units (P < .0001). CONCLUSIONS: Unit-specific estimates based on known predictors of HHO rate enabled broad implementation of GEHHMS. Further longitudinal quality improvement efforts using this system are required to assess the impact of GEHHMS on both HH adherence and clinical outcomes within critically ill patient populations.

Angiopoietin-1 and angiopoietin-2 as clinically informative prognostic biomarkers of morbidity and mortality in severe sepsis.

OBJECTIVE: To determine the utility of angiopoietin-1 and angiopoietin-2 as potentially novel biomarkers of morbidity and mortality in patients with severe sepsis. DESIGN: Multicenter longitudinal cohort study. SETTING: Three tertiary hospital intensive care units in Hamilton, Ontario, Canada. PATIENTS: A total of 70 patients with severe sepsis were enrolled within 24 hrs of meeting the inclusion criteria for severe sepsis and followed until day 28, hospital discharge, or death. INTERVENTIONS: Clinical data and plasma samples were obtained at intensive care unit admission for all 70 patients and then daily for 1 wk and weekly thereafter for a subset of 43 patients. Levels of angiopoietin-1 and angiopoietin-2 in stored plasma samples were measured and compared with clinical characteristics, including the primary outcomes of 28-day mortality and morbidity measured by the Multiple Organ Dysfunction score. MEASUREMENTS AND MAIN RESULTS: Lower angiopoietin-1 plasma levels (≤ 5.5 ng/mL) at admission were associated with increased likelihood of death (relative risk 0.49 [95% confidence interval of 0.25-0.98], p = .046). Lower angiopoietin-1 levels remained a significant predictor of 28-day mortality in a multiple logistic regression model (adjusted odds ratio of 0.282 [95% confidence interval of 0.086-0.93], p = .037). Analysis of serial data using linear mixed models confirmed that sepsis survivors had higher levels of angiopoietin-1 (p = .012) and lower daily levels of angiopoietin-2 (p = .022) than nonsurvivors. Furthermore, survivors had higher peak angiopoietin-1 levels (median 13 vs. 10 ng/mL, p = .019) and lower nadir angiopoietin-2 levels (median 2.8 vs. 6.2 ng/mL, p = .013) than nonsurvivors. A score incorporating angiopoietin-1 and angiopoietin-2 and three other markers of endothelial activation discriminated with high accuracy between fatal and nonfatal cases (c-index of 0.80 [95% confidence interval of 0.69-0.90], p < .001). Plasma levels of angiopoietin-2 correlated with clinical markers of organ dysfunction and molecular markers of endothelial cell activation. CONCLUSIONS: Angiopoietin-1 levels at admission and both angiopoietin-1 and angiopoietin-2 levels measured serially correlated with 28-day mortality in severe sepsis. Angiopoietin-2 levels also correlated with organ dysfunction/injury and a validated clinical sepsis score. These results suggest the use of angiopoietins as clinically informative biomarkers of disease severity and patient outcome in severe sepsis.