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Subtotal laparoscopic cholecystectomy (SUB) is an alternative to total laparoscopic cholecystectomy (TOT) when the critical view of safety (CVS) cannot be achieved. Little is known about the clinical factors and postoperative outcomes associated with SUB. The objective was to determine predictive factors and outcomes of SUB as compared to TOT.Clinical data from patients admitted from our emergency department to the acute care surgery service who underwent SUB or TOT by an acute care surgery surgeon for acute biliary disease (2017-2019) were reviewed. Wilcoxon rank-sum and Fisher's exact tests were used.355 patients underwent cholecystectomy for acute cholecystitis; 28 were SUB (7.9%). SUB patients were more likely to be older (57 versus 43 y; P = 0.015), male (60.7% versus 39.3%; P 0.001), have a history of cirrhosis or liver disease (14.3% versus 2.1%; P = 0.007), and have a higher Charlson-Comorbidity Index (1 versus 0, P = 0.041). SUB had greater leukocytosis (14.6 versus 10.9; P 0.001), higher total bilirubin (0.9 versus 0.6; P = 0.021), and a higher Tokyo grade (2 versus 1; P 0.001), and had operative findings including gallbladder decompression (82.1% versus 23.2%; P 0.001) and inability to achieve the CVS (78.6% versus 3.4%; P 0.001). SUB patients had an increased length of stay (4 versus 2 d; P 0.001) and more 1-y readmissions. No major vascular injuries occurred in either group with one biliary injury in the TOT group.SUB patients present with more significant markers of biliary disease and have more complicated intraoperative and postoperative courses. However, the lack of biliary or vascular injuries suggests that SUB may represent a safe alternative when the CVS cannot be achieved.

Accurate simulations of gross primary production (GPP) are vital in our efforts to model the global carbon cycle. The instantaneous controls of leaf-level photosynthesis, which can be studied in manipulative experiments, are well established; but there is no consensus on how canopy-level GPP depends on spatial and temporal variation in the environment. Models make a variety of assumptions when ‘scaling-up’ the standard model of photosynthesis. These assumptions are consequential, leading to large differences in the apparent environmental dependencies of modelled GPP.We have attempted to understand and resolve these inconsistencies using both theoretical analysis of the processes involved in scaling-up from photosynthesis to GPP, and empirical analysis by generalized linear modelling of GPP inferred from eddy-covariance flux measurements. Theoretical analysis has explained why ‘light-use efficiency’ (LUE) models work – and has led to the ‘P model’, a notably parsimonious model that coordinates capacities for CO2 fixation, water- and electron-transport to simulate GPP. For empirical analysis we used eddy-covariance data from over 100 sites worldwide. We combined these flux data with in situ radiation measurements and the MODIS FPAR product. Soil moisture data were estimated using the SPLASH model, with appropriate meteorological inputs, and soil water-holding capacity derived using SoilGrids.In arriving at a preferred statistical model, we showed that daytime air temperature and vapour pressure deficit, and soil moisture content are salient predictors of LUE. Despite taking LUE (GPP normalised for absorbed light) as our response variable, we found that the diffuse fraction of solar radiation has a strong influence on production: second only to VPD in predictive power. That finding challenges the idea, dating back 50 years to studies of crop yield, that time-averaged carbon assimilation is simply proportional to the amount of absorbed light. Our empirical analysis of GPP data has led us to seek ways to improve the performance of the P model without sacrificing its simplicity and transparency. Differential canopy penetration by diffuse and direct radiation is one line of development. Also needed is an improved representation of the temperature dependency of GPP. The empirical analysis suggested a generally increasing (asymptotic) trend over the observed range in growth temperature rather than the temperature optimum of 15°C displayed by the current P model simulations.Our analysis suggests it is feasible to predict GPP using a single model structure, common across vegetation categories. But the goal of a model design that is at once simple, theoretically well-founded and robust continues to generate scientific challenges.

Climate change is projected to cause not only higher mean temperatures but also higher climate variability. Although elevated CO2 concentrations can potentially increase the productivity of some ecosystems, higher temperatures and more frequent droughts may lead to increased respiration and mortality, possibly negating these productivity gains. The capacity of global forests to adjust to climate change depends on their functional diversity and the ecosystem’s adaptive capacity.The Plant-FATE eco-evolutionary model describes vegetation responses to altered environmental conditions, including CO2 concentrations, temperatures, and droughts. It represents functional diversity by modelling species as points in trait space and incorporates ecosystem adaptations at three levels: 1) to model acclimation of plastic traits of individual plants, we leverage the power of eco-evolutionary optimality principles, 2) to model shifts in species composition via demographic changes and species immigration, we implement a trait-size-structured demographic vegetation model, and 3) to model the long-term genetic evolution of species, we have developed new evolutionary theory for trait-size-structured communities.First, we show that with just a few calibrated parameters, the Plant-FATE model accurately predicts the fluxes of CO2 and water, size distributions, and trait distributions for a tropical wet site in the Amazon Forest. Second, we show that under elevated CO2 conditions and in the absence of nutrient limitation, our model predictions are broadly consistent with observations, namely: an increase in leaf area, productivity and biomass, and a decrease in stomatal conductance and photosynthetic capacity. Third, we simulate the calibrated model with hypothetical future drought regimes to investigate three key features of ecosystem responses: 1) the change in species composition and ecosystem functioning in response to altered conditions, 2) the timescales of ecosystem response to new regimes, 3) the influence of functional diversity on the timescale of ecosystem adaptation and its consequences for ecosystem collapse.Our eco-evolutionary vegetation modelling strategy presents a powerful approach to leverage the power of natural selection to simulate ecosystem dynamics under novel conditions that plants may have never experienced before.

Forests in Dynamic Global Vegetation Models (DGVMs) have historically been simulated as area-averaged plant functional types in each gridcell instead of representing communities of trees of different sizes and ages (demography). Just as the behaviour of a tree differs according to its ontogeny, so the behaviour of forests is known to differ depending on their demography. Accurately simulating demography is therefore key in order to address questions on afforestation and management strategies, as well as assessments of resilience of forests to disturbances such as drought and fire or diversity changes after a disturbance. Ultimately, demography determines the overall forest biomass in natural forests and is a key arbiter of growth and mortality rates. DGVMs are now able to simulate size and age structure of the trees in forests. However, these models have so far not been benchmarked alongside each other.We evaluate 6 DGVMs (BiomeE, CABLE-POP, FATES, LPJ-GUESS, JULES-RED, ORCHIDEE) against observations on regrowth dynamics as well as natural forests at boreal, temperate and tropical sites. We examine whether the models capture observed regrowth dynamics after disturbance, well-known stand size structure and well-established processes such as self-thinning. We outline the planned route forward towards a standardised international benchmarking framework for demographic DGVMs.

The increased frequency of temperature anomalies and drought events in Switzerland has major ecological implications, with impacts over whole ecosystems. In Swiss forests, the 2018 drought, which was the most severe drought event recorded led to widespread leaf discoloration, premature leaf-shedding, and tree mortality. While work has been carried out to analyse droughts a posteriori, the prediction of potential drought impacts would make it possible to anticipate ecological responses, manage resources and mitigate damage. Current approaches to drought prediction include mechanistic models. However, such models are often limited by data accessibility and resolution to effectively describe local effects. Deep learning models trained on remote sensing and atmospheric data have been applied to drought fore- casting, but face the “black box” issue and often discard domain knowledge on drought mechanisms. In this work, we propose a spatio-temporal deep learning method for drought forecasting in forests based on Sentinel-2 satellite imagery and weather variables, with the inclusion of topographic and environmental information. Drought is monitored by a proxy of early leaf wilting, using the normalized difference vegetation index (NDVI) that can be derived from Sentinel-2 bands. By predicting future NDVI values of pixels, we predict the potential occurrence of droughts in the short term. Hand-crafted features based on environmental data are used as input for the model, such as high-resolution topographic features which can capture micro-climatic effects, as well as soil- vegetation-climate relationships. Environmental information is provided to the model through data on soil and forest properties. This explicit modelling with topographic and environmental features increases the model interpretability, compared to models performing feature extraction and based only on image bands. A sequence model with long short-term memory (LSTM) cells was selected for its capacity to learn long-term dependencies as required in our application. We implement a pipeline to process spatiotemporal data, including data aggregation, normalization, missing data impu- tation and sample pixel timeseries for the prediction task. The model is trained and tested on data between 2015 and 2021, using the mean squared error to evaluate performances. A month (3 timesteps at Sentinel-2 acquisition rate) is forecasted given the past 3 months (9 timesteps) at a specific location. We opt for a “guided prediction” approach where the model has also access to weather forecasts for the future timesteps. The model is trained and tested in different regions in Switzerland to assess its generalization in space. A feature importance study was performed to identify key factors for drought forecasting and further improve the model. This research combines drought predictors known to have an impact in ecology and hydrology with a guided deep learning model. We offer a method for dealing with heterogeneous spatiotemporal data and train an interpretable model for forecasting potential forest drought.

Recent hot and dry summers in Europe have had a significant impact on forest functioning and structure. In 2018 and 2019, Central Europe experienced two extremely dry and hot summers. These extremes resulted in widespread canopy defoliation and tree mortality. The objective in this study is to create a predictive model for predicting the density of vegetation, as measured by the NDVI index. We predict NDVI at a horizon of a month utilising data from the previous months as input to determine where and when drought impacts are triggered. Such predictive models should take into account both spatial and temporal dependencies between environmental variables and impacts. We hereon focus on Switzerland's forests as a region of interest to leverage high-quality model input layers and applications to typical stakeholder needs. Widely used vegetation indices and mechanistic land surface models are not effectively informed by the full information contained in Earth Observation data and the observed spatial heterogeneity of land surface greenness responses at hillslope-scale resolution. Effective learning from the simultaneous evolution of climate and remotely sensed land surface properties is challenging. Modern deep learning and machine learning techniques, however, have the capacity to generate accurate predictions while also explaining the relationship between climate and its recent history, the position in the landscape, and influences on vegetation. The task is to predict the future NDVI over forest areas, given past and future weather and surface reflectance. Giving future weather predictions as an input to the model, we are going for a 'guided-prediction' approach where the aim is to exploit weather information from forecasting models in order to increase the predictive power of the model - similar to the EarthNet2021 Challenge. Models are fully data-driven, without feature engineering and trained on spatio-temporal datacubes which can be seen as stacked satellite imagery for a specific geo-location and a timestep of past Sentinel 2 surface reflectance, past (observed) and future (forecasted) climate reanalysis, time-invariant information from a digital elevation model, and land cover map. The data pre-processing step includes implementing a customized dataset for drought impact prediction task, and a customized data sampler in order to be able to sample data (scenes) both spatially and temporally. Additional data operations include aggregation of the weather data, normalization, and data imputation both on the image-level and missing-day level. For the prediction task, we used Convolutional Long-Short Term Memory models. In the temporal domain, models are trained on the period between 2015-2018, and be validated between 05-2019 and 09-2019. For the test period summer months of 2020 and 2021 will be used. However, in the spatial domain, for the sake of testing the generalizability of the model, different regions were used for train, validate and test processes. In order to asses the models performance on the temporal domain, tests with different training and testing window sizes are used. As for evaluating the performance of the model, Mean Squared Error was used. The project will lay the basis for an early warning platform to enable periodically updated near-term drought-impact forecasts.

Water availability controls vegetation activity and the carbon balance of terrestrial ecosystems across a large portion of the global land surface. Although the influence of terrestrial water storage (TWS) on the land carbon balance is evident in globally aggregated measures, it remains unknown whether the large annual amplitudes in TWS are causally linked to water availability in the rooting zone of vegetation, or whether they reflect a correlation of plant water stress with water stored in other landscape elements that may not directly be connected to vegetation functioning (lakes, rivers, groundwater). Global models of the land surface typically ignore hillslope-scale variations in plant water availability, and water stores that are located beyond the soil, and beyond prescribed plant rooting depths. This simplification is partly owed to a lack of empirical information.Here, we approach this gap from two angles: from the site scale using eddy covariance observations, and from the global scale using earth observations. Water mass balance constraints derived from thermal infrared-based evapotranspiration (ET) estimates and precipitation reanalysis data indicate plant-available water stores that exceed the storage capacity of 2 m deep soils across 37% of the Earth’s vegetated surface. Large spatial variations of the rooting zone water storage capacity across topographic and hydro-climatic gradients are tightly linked to the sensitivity of vegetation activity (measured by sun-induced fluorescence and by the evaporative fraction) to water deficits. Similar patterns between ET and cumulative water deficits emerge from site-level flux measurements. We found large variations of the vegetation sensitivity to dry conditions across sites and at several sites a muted response of ET to dry conditions in spite of large (>300 mm) seasonal water deficits at some sites.Taken together, results we show here hint at a critical role of plant access to deep water stores and the need to extend the focus beyond moisture in the top 1-2 m of soil for understanding and simulating land-atmosphere exchange. Our results add to the emerging evidence that water stored in the weathered bedrock and plant access to groundwater may have a more important role in regulating land-atmosphere exchange and the carbon cycle than previously appreciated.

Ecosystem manipulative experiments are one of the most powerful tools to understand terrestrial ecosystem responses to global change because they measure real responses in real ecosystems. However, their scope is limited in space and time due to cost and labour intensity. This makes generalising results from such experiments difficult, which creates a conceptual gap between local scale process understanding and global scale future predictions. Recent efforts have seen results from such experiments used in combination with dynamic global vegetation models, most commonly to evaluate model predictions under global change drivers. However, there is much more potential in combining models and experiments. Here, we discuss the value and potential of a workflow for using ecosystem experiments together with process-based models to enhance the potential of both. We suggest that models can be used prior to the start of an experiment to generate hypotheses, identify data needs and in general guide experimental design. Models, when adequately constrained with observations, can also predict variables which are difficult to measure frequently or at all, and together with the data can provide a more complete picture of ecosystem states. Finally, models can be used to help generalise the experimental results in space and time, by providing a framework in which process understanding derived from site-level experiments can be incorporated. We also discuss the potential for using manipulative experiments together with models in formalised model-data integration frameworks for parameter estimation and model selection, a path made possible by the increasing number of ecosystem experiments and diverse observation streams. The ideas presented here can provide a roadmap to future experiment - model studies.

Background The American Association for the Surgery of Trauma (AAST) has developed a grading system for emergency general surgery (EGS) conditions. We sought to validate the AAST EGS grades for patients undergoing urgent/emergent colorectal resection. Methods Patients enrolled in the “Eastern Association for the Surgery of Trauma Multicenter Colorectal Resection in EGS—to anastomose or not to anastomose” study undergoing urgent/emergent surgery for obstruction, ischemia, or diverticulitis were included. Baseline demographics, comorbidity severity as defined by Charlson comorbidity index (CCI), procedure type, and AAST grade were prospectively collected. Outcomes included length of stay (LOS) in-hospital mortality, and surgical complications (superficial/deep/organ-space surgical site infection, anastomotic leak, stoma complication, fascial dehiscence, and need for further intervention). Multivariable logistic regression models were used to describe outcomes and risk factors for surgical complication or mortality. Results There were 367 patients, with a mean (± SD) age of 62 ± 15 years. 39% were women. The median interquartile range (IQR) CCI was 4 (2-6). Overall, the pathologies encompassed the following AAST EGS grades: I (17, 5%), II (54, 15%), III (115, 31%), IV (95, 26%), and V (86, 23%). Management included laparoscopic (24, 7%), open (319, 87%), and laparoscopy converted to laparotomy (24, 6%). Higher AAST grade was associated with laparotomy ( P = .01). The median LOS was 13 days (8-22). At least 1 surgical complication occurred in 33% of patients and the mortality rate was 14%. Development of at least 1 surgical complication, need for unplanned intervention, mortality, and increased LOS were associated with increasing AAST severity grade. On multivariable analysis, factors predictive of in-hospital mortality included AAST organ grade, CCI, and preoperative vasopressor use (odds ratio (OR) 1.9, 1.6, 3.1, respectively). The American Association for the Surgery of Trauma emergency general surgery grade was also associated with the development of at least 1 surgical complication (OR 2.5), while CCI, preoperative vasopressor use, respiratory failure, and pneumoperitoneum were not. Conclusion The American Association for the Surgery of Trauma emergency general surgery grading systems display construct validity for mortality and surgical complications after urgent/emergent colorectal resection. These results support incorporation of AAST EGS grades for quality benchmarking and surgical outcomes research.

Responses of the terrestrial biosphere to rapidly changing environmental conditions are a major source of uncertainty in climate projections. In an effort to reduce this uncertainty, a wide range of global change experiments have been conducted that mimic future conditions in terrestrial ecosystems, manipulating CO2 , temperature, and nutrient and water availability. Syntheses of results across experiments provide a more general sense of ecosystem responses to global change, and help to discern the influence of background conditions such as climate and vegetation type in determining global change responses. Several independent syntheses of published data have yielded distinct databases for specific objectives. Such parallel, uncoordinated initiatives carry the risk of producing redundant data collection efforts and have led to contrasting outcomes without clarifying the underlying reason for divergence. These problems could be avoided by creating a publicly available, updatable, curated database. Here, we report on a global effort to collect and curate 57,089 treatment responses across 3644 manipulation experiments at 1145 sites, simulating elevated CO2 , warming, nutrient addition, and precipitation changes. In the resulting Manipulation Experiments Synthesis Initiative (MESI) database, effects of experimental global change drivers on carbon and nutrient cycles are included, as well as ancillary data such as background climate, vegetation type, treatment magnitude, duration, and, unique to our database, measured soil properties. Our analysis of the database indicates that most experiments are short term (one or few growing seasons), conducted in the USA, Europe, or China, and that the most abundantly reported variable is aboveground biomass. We provide the most comprehensive multifactor global change database to date, enabling the research community to tackle open research questions, vital to global policymaking. The MESI database, freely accessible at doi.org/10.5281/zenodo.7153253, opens new avenues for model evaluation and synthesis-based understanding of how global change affects terrestrial biomes. We welcome contributions to the database on GitHub. ispartof: GLOBAL CHANGE BIOLOGY vol:29 issue:7 pages:1922-1938 ispartof: location:England status: accepted

Changes in discharge disposition and delays in discharge negatively impact the patient and hospital system. Our objectives wereDischarge dispositions and barriers to discharge for 200 TEGS patients were predicted individually by members of the multidisciplinary TEGS team within 24 h of patient admission. Univariate analyses and multivariable logistic least absolute shrinkage and selection operator regressions determined the associations between patient characteristics and correct predictions.A total of 1,498 predictions of discharge disposition were made by the multidisciplinary TEGS team for 200 TEGS patients. Providers correctly predicted 74% of discharge dispositions. Prediction accuracy was not associated with clinical experience or job title. Incorrect predictions were independently associated with older age (OR 0.98; P0.001), trauma admission as compared to emergency general surgery (OR 0.33; P0.001), higher Injury Severity Scores (OR 0.96; P0.001), longer lengths of stay (OR 0.90; P0.001), frailty (OR 0.43; P = 0.001), ICU admission (OR 0.54; P0.001), and higher Acute Physiology and Chronic Health Evaluation II scores (OR 0.94; P = 0.006).The TEGS team can accurately predict the majority of discharge dispositions. Patients with risk factors for unpredictable dispositions should be flagged to better allocate appropriate resources and more intensively plan their discharges.

Recent syntheses of elevated CO2 (eCO2) experiments provide critical information to quantify the effects of rising CO2 on vegetation. However, although the eCO2 effect on aboveground biomass (βAGB) and soil carbon pool (βCsoil) have been estimated, little has been known about the eCO2 effect on total net primary production (βTNPP) - the sum of above and belowground - due to the difficulties of measuring NPP in eCO2 experiments. As a preliminary analysis of βTNPP, we ran a thorough meta-analysis of global eCO2 experiments and found that (1) Most of the experiments reported a larger βTNPP than βAGB (2) Most of the experiments reported a larger eCO2 effect on belowground (roots) biomass (βBGB) than total biomass (βTB) (for AM mycorrhizal association only) (3) On average, seedling experiments reported a larger βAGB than mature vegetation experiments. Aiming to improve estimates of βTNPP we hypothesized that βAGB could be underestimated due to short experimental time relative to biomass mean residence time, i.e., that βAGB increases with experimental time. We proposed a set of simple first kinetics equation to derive βTNPP with consideration of the imbalance between βBGB and βTB. We first validated the equations on experiments reporting both productivity and biomass before applying to other experiments. We found that, after the application, (1) On average, βTNPP of seedling experiments are insignificantly different to βTNPP of mature vegetation experiments (2) Compared with βAGB, the relative importance of mycorrhizal on βTNPP decrease, and soil CN ratio and soil phosphorus became the most important predictors. (3) The disagreement between experiments and models was smaller for βTNPP than for βAGB.

Photosynthetic acclimation under CO2 fertilization is still incompletely understood. Observed reductions in the maximum rate of carboxylation (Vcmax) and electron transport (Jmax) under elevated CO2 (often called ‘down-regulation’) have been explained in various ways, including limited soil nitrogen (N) and phosphorus (P) availability, or a reduced demand for N. However, there remains large variation of the Vcmax decline and some non-sensitive or even positive responses are documented. The least cost hypothesis (Prentice et al., 2014) states that optimal photosynthesis is achieved at balanced unit costs of capacities of carboxylation and transpiration and predicts the acclimation of Vcmax and Jmax in response to the environment. Under elevated CO2, Vcmax is predicted to decline, while the ratio Jmax/Vcmax is predicted to increase - independent of N supply from the soil. In contrast, common model parametrisations conceive Vcmax to be controlled by soil N supply.Here, we analyse a compilation of experimental results in an attempt to better understand photosynthetic acclimation to elevated CO2 and balance the evidence for contrasting model formulations. Within 38 CO2 fertilization plots investigated at forest, grassland and cropland, Vcmax and Jmax are shown to decrease in concert, while the ratio Jmax/Vcmax increases with higher CO2 concentration, consistent with predictions from the least cost hypothesis. However, the predicted increase in the Jmax/Vcmax ratio is too large and the observed change in Vcmax is correlated with the change in soil inorganic N. Observed leaf N responses are broadly consistent with changes in Vcmax and Jmax. These findings support the idea acclimation of photosynthetic traits under enhanced CO2 is modulated by soil N supply. This can be explained by the direct decline of soil N availability at higher CO2 concentrations. However, it may also be caused by increase rates of net primary production (NPP) and N uptake that increase N sequestered in biomass under elevated CO2, in such a way to constrain labile soil N available for leaf-level photosynthesis.Vcmax and Jmax responses to CO2 were also found to be negatively related to increases of above- and below-ground net primary production (ANPP, BNPP). This pattern might be explained by a ‘dilution effect’, due to a CO2-induced increase of leaf area index (LAI). However, it might also be due to plants having different stomatal responses to CO2. According to this hypothesis, at one end of the spectrum, the ratio of leaf intercellular CO2 (Ci) relative to ambient CO2 (Ca) remains constant; optimal photosynthesis increases, while optimal Vcmax declines. At the other end of the spectrum, Ci/Ca decreases enough that Ci remains constant; then there is no increase in optimal photosynthesis, and no change in optimal Vcmax. Testing this hypothesis would require concomitant measurements of all of the relevant quantities (LAI, NPP, Ci/Ca) in multiple experiments.

The projected extension of growing season length under climate change will increase the carbon uptake potential of forest ecosystems at high latitudes. However, the dynamics of carbon uptake in high latitudes, namely ecosystem scale photosynthesis have not been well represented in existing carbon models yet. This impedes the unbiased assessment of current and predicted carbon dynamics at regional and continental scales. For instance, a substantial overestimation of simulated (by applying a state-of-the-art terrestrial photosynthesis model (P-model)) compared to gross primary productivity (GPP) derived from eddy-covariance (EC) was found in early spring for many sites and years. Here we show a substantial reduction of this GPP overestimation by adding a stress function accounting for the effects of low temperature and high light intensity. We found the strong depression of light use efficiency (LUE) in the GPP overestimation sites, which is related to the high photosynthetic photon flux density (PPFD) and low daily minimum temperatures (Tmin) during the early spring and preceding weeks or months. This mismatch between modelled and EC-derived GPP can be attributed to the adjustment of leaf properties and photosynthesis, e.g., synthesizing photoprotective pigments while reducing photosynthesis pigments in evergreen trees in the cold periods to protect the photosynthesis apparatus from damage from excessive light (photoprotection). This is supported by the observed high red chromatic coordinates (RCC) and delayed increase of green chromatic coordinates (GCC) from digital repeat photography. Finally, through embedding an empirical stress function considering the direct and lagged impact from Tmin into the model, we improved the GPP estimation and reduced the model-observation mismatch in GPP in early spring. Our results demonstrate one way to improve the GPP estimation in high latitude ecosystem by taking the physiological processes (e.g. photoprotection) into account. This enables a more accurate estimation of the continental carbon dynamics under climate change.

Climate change is causing shifts in the growing seasons of plants1,2, affecting species performance and interactions3,4 as well as global carbon, water and nutrient cycles5,6. How the timing of autumn leaf senescence in extra-tropical forests will change remains unclear because of the complex seasonal interaction of climate warming, earlier and enhanced vegetation activity, and the annual day-length cycle7–10. Here we use experiments, long-term ground observations, and satellite-based vegetation monitoring to show that early-season and late-season warming have opposite effects on the onset and progression of leaf senescence, with a reversal occurring at the year’s longest day (summer solstice). Across ~84% of the northern forest area, increased temperature and vegetation activity before the solstice led to an earlier senescence onset (10% greenness loss) of, on average, –1.6 ± 0.1 days-per-°C, while warmer post-solstice temperatures did not affect senescence onset but reduced its speed (progression to 50% greenness loss) by 0.8 ± 0.1 days-per-°C. Due to the earlier senescence onset, the day at which autumn temperature starts driving senescence progression has been shifting to ever earlier dates, between 1951—2015 at a rate of –0.20 ± 0.07 days per year. These developmental constraints suggest that senescence will start earlier but progress more slowly in the future, revealing Northern Hemisphere-wide compensation effects on trends in growing-season length, caused by enhanced pre-solstice vegetation activity. This new mechanistic insight improves our ability to model carbon uptake by extra-tropical forests under climate change.

Southeast Asia is a region known for active land-use changes (LUC) over the past 60 years; yet, how trends in net CO2 uptake and release resulting from LUC activities (net LUC flux) have changed through past decades remains uncertain. The level of uncertainty in net LUC flux from process-based models is so high that it cannot be concluded that newer estimates are necessarily more reliable than older ones. Here, we examined net LUC flux estimates of Southeast Asia for the 1980s−2010s from older and newer sets of Dynamic Global Vegetation Model simulations (TRENDY v2 and v7, respectively), and forcing data used for running those simulations, along with two book-keeping estimates (H&N and BLUE). These estimates yielded two contrasting historical LUC transitions, such that TRENDY v2 and H&N showed a transition from increased emissions from the 1980s to 1990s to declining emissions in the 2000s, while TRENDY v7 and BLUE showed the opposite transition. We found that these contrasting transitions originated in the update of LUC forcing data, which reduced the loss of forest area during the 1990s. Further evaluation of remote sensing studies, atmospheric inversions, and the history of forestry and environmental policies in Southeast Asia supported the occurrence of peak emissions in the 1990s and declining thereafter. However, whether LUC emissions continue to decline in Southeast Asia remains uncertain as key processes in recent years, such as conversion of peat forest to oil-palm plantation, are yet to be represented in the forcing data, suggesting a need for further revision., Global Biogeochemical Cycles, 36 (1), ISSN:0886-6236, ISSN:1944-9224

OBJECTIVE: Risk factors for mortality in patients with subdural hematoma (SDH) include poor Glasgow Coma Scale (GCS), pupil non-reactivity, and hemodynamic instability on presentation. Little is published regarding prognosticators of SDH in the elderly. This study aims to examine risk factors for hospital mortality and withdrawal of life-sustaining measures in an octogenarian population presenting with SDH. METHODS: A prospectively collected multi-center database of 3,279 TBI admissions to 45 different U.S. trauma centers between 2017–2019 was queried to identify patients >79 years old presenting with SDH. Factors collected included baseline demographic data, past medical history, antiplatelet/anticoagulant use, and clinical presentation (GCS, pupil reactivity, injury severity scale [ISS]). Primary outcome data included hospital mortality/discharge to hospice care and withdrawal of life-sustaining measures. Multivariate logistic regression analyses were used to identify factors independently associated with primary outcome variables. RESULTS: A total of 695 patients were isolated for analysis. Of the total cohort, the rate of hospital mortality or discharge to hospice care was 22% (n=150) and the rate of withdrawal of life-sustaining measures was 10% (n=66). A multivariate logistic regression model identified GCS 80 years with SDH. Pre-existing CHF may further predict withdrawal of life-sustaining measures.

Using traditional photosynthesis-intercellular CO concentration (A-C) response (TACiR) curves to obtain the maximum rates of ribulose-1,5-bisphosphate carboxylase oxygenase carboxylation (V) and electron transport (J) is time-consuming and labor-intensive. Instead, the rapid A-C response (RACiR) technique provides a potential way with high efficiency. However, multiple parameter settings of RACiR technique for different plant life forms remain unclear. Here, we used Li-Cor 6800 to test the applicability and optimum parameter settings of RACiR curves for evergreens and herbs. We set 11 groups of [CO], i.e., R1 (400-1500 ppm), R2 (400-200-800 ppm), R3 (420-20-620 ppm), R4 (420-20-820 ppm), R5 (420-20-1020 ppm), R6 (420-20-1220 ppm), R7 (420-20-1520 ppm), R8 (420-20-1820 ppm), R9 (450-50-650 ppm), R10 (650-50 ppm) and R11 (650-50-650 ppm), and made contrasts between TACiR and RACiR curves. We found that V and J calculated by TACiR and RACiR overall showed no significant differences across 11 [CO] gradients (P

Codes and data for global mapping of root-shoot ratios

ObjectiveEvidence comparing stoma creation (STM) versus anastomosis after urgent or emergent colorectal resection is limited. This study examined outcomes after colorectal resection in emergency general surgery patients.MethodsThis was an Eastern Association for the Surgery of Trauma-sponsored prospective observational multicenter study of patients undergoing urgent/emergent colorectal resection. Twenty-one centers enrolled patients for 11 months. Preoperative, intraoperative, and postoperative variables were recorded. χ, Mann-Whitney U test, and multivariable logistic regression models were used to describe outcomes and risk factors for surgical complication/mortality.ResultsA total of 439 patients were enrolled (ANST, 184; STM, 255). The median (interquartile range) age was 62 (53-71) years, and the median Charlson Comorbidity Index (CCI) was 4 (1-6). The most common indication for surgery was diverticulitis (28%). Stoma group was older (64 vs. 58 years, p < 0.001), had a higher CCI, and were more likely to be immunosuppressed. Preoperatively, STM patients were more likely to be intubated (57 vs. 15, p < 0.001), on vasopressors (61 vs. 13, p < 0.001), have pneumoperitoneum (131 vs. 41, p < 0.001) or fecal contamination (114 vs. 33, p < 0.001), and had a higher incidence of elevated lactate (149 vs. 67, p < 0.001). Overall mortality was 13%, which was higher in STM patients (18% vs. 8%, p = 0.02). Surgical complications were more common in STM patients (35% vs. 25%, p = 0.02). On multivariable analysis, management with an open abdomen, intraoperative blood transfusion, and larger hospital size were associated with development of a surgical complication, while CCI, preoperative vasopressor use, steroid use, open abdomen, and intraoperative blood transfusion were independently associated with mortality.ConclusionThis study highlights a tendency to perform fecal diversion in patients who are acutely ill at presentation. There is a higher morbidity and mortality rate in STM patients. Independent predictors of mortality include CCI, preoperative vasopressor use, steroid use, open abdomen, and intraoperative blood transfusion. Following adjustment by clinical factors, method of colon management was not associated with surgical complications or mortality.Level of evidenceTherapeutic study, level IV.

Describe the epidemiology of a large cohort of older adults with isolated traumatic brain injury (TBI) and identify predictors of mortality, palliative interventions, and discharge to preinjury residence in those presenting with moderate/severe TBI.Prospective observational study of geriatric patients with TBI enrolled across 45 trauma centers.Inclusion criteria were age ≥40 years, and computed tomography (CT)-verified TBI. Exclusion criteria were any other body region abbreviated injury scale score2 and presentation at enrolling center24 hours after injury.The analysis was restricted to individuals aged ≥65 and stratified into 3 age groups: young-old (65-74), middle-old (75-84), and oldest-old (≥85). Demographic, clinical, and injury data were collected. Predictors of mortality, palliative interventions, and discharge to preinjury residence in the moderate/severe TBI group were identified using Classification and Regression Tree and Generalized Linear Mixed Models.Of the 3081 subjects enrolled in the study, 2028 were ≥65 years old. Overall, 339 (16.7%) presented with a moderate/severe TBI and experienced a 64% mortality rate. A Glasgow Coma Scale (GCS) score9 was the main predictor of mortality, CT worsening (odds ratio [OR] = 1.7, P.04), cerebral edema (OR = 2.4, P.04), GCS9, and age ≥75 (OR = 2.1, P = .007) were predictors for palliative interventions, and an injury severity score ≤24 (OR = 0.087, P = .002) was associated with increased likelihood of discharge to preinjury residence in the moderate/severe TBI group.In this prospective study of a large cohort of older adults with isolated TBI, comparisons across the older age groups with moderate/severe TBI revealed that survival and favorable discharge disposition were influenced more by severity of injury rather than age itself. Indicating that chronological age alone maybe insufficient to accurately predict outcomes, and increased representation of older adults in TBI research to develop better diagnostic and prognostic tools is warranted.

Using high precision and centennial resolution ice core information on atmospheric nitrous oxide concentrations and its stable nitrogen and oxygen isotopic composition enables us to quantitatively reconstruct changes in the terrestrial and marine N2O emissions over the last 21,000 years as well as over Heinrich Stadial (HS) 4.We show that over the deglaciation N2O emissions from land and ocean increased in parallel by 1.8 ± 0.3 TgN yr-1 and 0.7 ± 0.3 TgN yr-1, respectively. However, close to 50% of the terrestrial increase is accomplished within less than 200 years at the end of HS1 starting essentially in parallel with the co-occurring CH4 increase. A similarly rapid but smaller increase is observed at the end of HS0 and suggested at the end of HS4, showing that terrestrial N2O emissions respond strongly and rapidly to the northward shift in the Intertropical Convergence Zone connected to the resumption of the Atlantic Meridional Overturning Circulation (AMOC). However, little change in terrestrial N2O emissions is observed during the onsets of Heinrich Stadials. Assuming that N2O loss from terrestrial ecosystems is directly connected to nitrogen turnover in soils, the fast increase at the end of Heinrich Stadials suggests that terrestrial ecosystems did not become nitrogen-limited on this relatively short time scales, as also supported by model runs in our LPX-Bern dynamic vegetation/biogeochemical model. However, changes in number of moles of N2O lost to the atmosphere per mole nitrogen turned over in soils (yield factor) may also contribute to the atmospheric N2O changes.Marine N2O emissions also respond to Heinrich events and AMOC changes, however more gradually and less strongly compared to terrestrial emissions both in our data-based reconstruction and the Bern3D coupled ocean/biogeochemistry model. In fact, reconstructed marine emissions start to slowly increase many centuries before the rapid warmings, connected to a re-equilibration of subsurface oxygen concentrations in response to previous AMOC changes. At the onset of HS1 marine emissions decreased by about 0.5 TgN yr-1, concomitantly with changes in atmospheric CO2 and δ13C(CO2), and started to re-increase after about 1500 years, when also rapid CO2 and CH4 jumps occurred, pointing to Southern Ocean and low-latitude circulation changes. A similar decrease as at the start of HS1 is found after the onset of HS0, but little N2O emission change is suggested by N2O concentrations and their isotopic signature at 39.5 kyr before present when Heinrich Event 4 presumably occurred, as suggested by a co-occurring intermittent CH4 peak and a sudden increase in CO2.

Simulation-based education can augment residents' skills and knowledge. We assessed the effectiveness of a simulation-based course for surgery interns designed to improve their comfort, knowledge, and ability to manage common surgical critical care (SCC) conditions.For 2 y, all first year residents (n = 31) in general surgery, urology, interventional radiology, and the integrated plastics, vascular, and cardiothoracic surgery training programs at our institution participated in a simulation-based course emphasizing evidence-based management of SCC conditions. Precourse and postcourse surveys and multiple-choice tests, as well as summative simulation tests, assessed interns' comfort, knowledge, and ability to manage SCC conditions. Changes in these measures were assessed with Wilcoxon matched-pairs signed rank tests. Factors associated with summative performance were determined by linear regression.The course consisted of four simulation-based teaching sessions in year 1 and six in year 2. The course taught seven of the 18 core SCC conditions in the Surgical Council on Resident Education general surgery curriculum in year 1 and 10 in year 2. Interns' self-reported comfort, knowledge, and ability to manage each condition taught in the course increased (P 0.02). Their knowledge of each condition, as assessed by written tests, also increased (P 0.02). Their summative simulation test performance correlated with the number of course sessions attended (P 0.03) and status as general surgery residents (P 0.01).A simulation-based SCC training course for surgery interns that emphasizes evidence-based management of SCC conditions improves interns' comfort, knowledge, and ability to manage these conditions.

Background Predicting length of stay (LOS) is difficult for trauma and emergency general surgery (TEGS) patients. Our aim was to determine the accuracy of LOS predictions by TEGS team members and the NSQIP Risk Calculator and the patient factors associated with inaccurate predictions. Methods LOS for 200 TEGS patients were predicted. Full-model univariate and multivariable linear regressions were used to determine associations between patient characteristics and inaccurate predictions. Results There were 1,518 predictions of LOS. LOS predictions were rarely correct (TEGS team: 30.7% all patients, 35.6% surgical; NSQIP: 33.0% surgical). No individual group nor NSQIP was significantly better at predicting LOS. Inaccurate predictions were associated with female patients, longer LOS, trauma, frailty, higher comorbidity and injury severity scores, and lesser disposition. Conclusion Both the TEGS team and NSQIP are poor at predicting LOS for TEGS patients. Further work helping to guide LOS predictions for TEGS patients is warranted.

Background Screening patients for frailty is traditionally done at the bedside. However, recent electronic medical record (EMR)-based, comorbidity-focused frailty assessments have been developed. Our objective was to determine how a common bedside frailty assessment, the trauma and emergency surgery (TEGS) frailty index (FI), compares to an EMR-based frailty assessment in predicting geriatric TEGS outcomes. Materials and methods We retrospectively reviewed our quality improvement project database consisting of TEGS patients ≥ 65 y old. Patients were screened with the TEGS FI, a 15-question bedside assessment, including comorbidities, physical activity, emotional health, and nutrition. Six of 15 items were retrievable from the enterprise data warehouse (EDW), storing all EMR data from Northwestern Memorial Hospital, and use to calculate the EDW frailty score. Patient characteristics and outcomes were compared between different groups. Results Two hundred thirty-six geriatric TEGS patients were included, of which 75 (31.8%) were TEGS FI frail and 60 (25.4%) were EDW frail. TEGS FI frail patients had increased length of stay (LOS), loss of independence (LOI), and complications compared to TEGS FI nonfrail patients. EDW frail patients had higher LOS and complications than EDW nonfrail patients but similar LOI. TEGS FI and EDW frail patients had similar outcomes except TEGS FI–only patients more often have LOI. Conclusions Bedside frailty assessments and EMR-based assessments are both effective in identifying geriatric TEGS patients at risk for increased LOS and complications. However, bedside frailty screening was better at identifying patients who have LOI and may be a more appropriate choice when screening for frailty.

Three dimensional hydrogels better replicate in vivo conditions, and yield different results from two dimensional substrates. However, imaging interactions between cells and the hydrogel microenvironment is challenging because of light diffraction and poor focal depth. Here, cryosectioning and vibrating microtomy methods and fixation protocols were compared. Collagen I/III hydrogel sections (20–100 µm) were fixed with paraformaldehyde (2–4%) and structurally evaluated. Cryosectioning damaged hydrogels, and vibrating microtomy (100 µm, 2%) yielded the best preservation of microstructure and cell integrity. These results demonstrate a potential processing method that preserves hydrogel and cell integrity, permitting imaging of cell interactions with the microenvironment.

Bone defects can originate from a variety of causes, including trauma, cancer, congenital deformity, and surgical reconstruction. Success of the current “gold standard” treatment (i.e., autologous bone grafts) is greatly influenced by insufficient or inappropriate bone stock. There is thus a critical need for the development of new, engineered materials for bone repair. This review describes the use of natural and synthetic hydrogels as scaffolds for bone tissue engineering. We discuss many of the advantages that hydrogels offer as bone repair materials, including their potential for osteoconductivity, biodegradability, controlled growth factor release, and cell encapsulation. We also discuss the use of hydrogels in composite devices with metals, ceramics, or polymers. These composites are useful because of the low mechanical moduli of hydrogels. Finally, the potential for thermosetting and photo-cross-linked hydrogels as three-dimensionally (3D) printed, patient-specific devices is highlighted. Three-dimensional printing enables controlled spatial distribution of scaffold materials, cells, and growth factors. Hydrogels, especially natural hydrogels present in bone matrix, have great potential to augment existing bone tissue engineering devices for the treatment of critical size bone defects.

As an alternative to ad hoc wireless sensor networks, we propose to utilize the mobile devices that are carried by people who walk along the site on which the sensors are deployed. Each sensor establishes a wireless connection to a mobile station and transmits its current measurements. On encounters two mobile stations can exchange their data for increasing redundancy and likeliness of delivery. When a mobile station approaches the sensor network server, it unloads all data which is saved there in a data base, processed, and published in local or global networks. In this paper, we describe our realization of this approach, the communication mechanisms that have been developed for it, as well as its potential usage. This approach is mainly characterized by the smooth integration of an additional data service in existing work processes utilizing only commercial off-the-shelf components.