Characteristics and Predictors of Intensive Care Unit Admission in Pediatric Blunt Abdominal Trauma

BACKGROUND: Pediatric trauma patients sustaining blunt abdominal trauma (BAT) with intra-abdominal injury (IAI) are frequently admitted to the intensive care unit (ICU). This study was performed to identify predictors for ICU admission following BAT. METHODS: Prospective study of children (\u3c 16 years) who presented to 14 Level-One Pediatric Trauma Centers following BAT over a 1-year period. Patients were categorized as ICU or non-ICU patients. Data collected included vitals, physical exam findings, laboratory results, imaging, and traumatic injuries. A multivariable hierarchical logistic regression model was used to identify predictors of ICU admission. Predictive ability of the model was assessed via tenfold cross-validated area under the receiver operating characteristic curves (cvAUC). RESULTS: Included were 2,182 children with 21% (n = 463) admitted to the ICU. On univariate analysis, ICU patients were associated with abnormal age-adjusted shock index, increased injury severity scores (ISS), lower Glasgow coma scores (GCS), traumatic brain injury (TBI), and severe solid organ injury (SOI). With multivariable logistic regression, factors associated with ICU admission were severe trauma (ISS \u3e 15), anemia (hematocrit \u3c 30), severe TBI (GCS \u3c 8), cervical spine injury, skull fracture, and severe solid organ injury. The cvAUC for the multivariable model was 0.91 (95% CI 0.88-0.92). CONCLUSION: Severe solid organ injury and traumatic brain injury, in association with multisystem trauma, appear to drive ICU admission in pediatric patients with BAT. These results may inform the design of a trauma bay prediction rule to assist in optimizing ICU resource utilization after BAT. STUDY DESIGN: Prognosis study

Exploring the oxidation behavior of undiluted and diluted iron particles for energy storage: Mössbauer spectroscopic analysis and kinetic modeling

Iron is an abundant and non-toxic element that holds great potential as energy carrier for large-scale and long-term energy storage. While from a general viewpoint iron oxidation is well-known, the detailed kinetics of oxidation for micrometer sized particles are missing, but required to enable large-scale utilization for energy production. In this work, iron particles are subjected to temperature-programmed oxidation. By dilution with boron nitride a sintering of the particles is prevented enabling to follow single particle effects. The mass fractions of iron and its oxides are determined for different oxidation times using Mössbauer spectroscopy. On the basis of the extracted phase compositions obtained at different times and temperatures (600–700 °C), it can be concluded that also for particles the oxidation follows a parabolic rate law. The parabolic rate constants are determined in this transition region. Knowledge of the particle size distribution and its consideration in modeling the oxidation kinetics of iron powder has proven to be crucial

Microplastics persist in an arable soil but do not affect soil microbial biomass, enzyme activities, and crop yield

Microplastics (MP, plastic particles <5 mm) are ubiquitous in arable soils due to significant inputs via organic fertilizers, sewage sludges, and plastic mulches. However, knowledge of typical MP loadings, their fate, and ecological impacts on arable soils is limited. We studied (1) MP background concentrations, (2) the fate of added conventional and biodegradable MP, and (3) effects of MP in combination with organic fertilizers on microbial abundance and activity associated with carbon (C) cycling, and crop yields in an arable soil. On a conventionally managed soil (Luvisol, silt loam), we arranged plots in a randomized complete block design with the following MP treatments (none, low‐density polyethylene [LDPE], a blend of poly(lactic acid) and poly(butylene adipate‐co‐terephthalate) [PLA/PBAT]) and organic fertilizers (none, compost, digestate). We added 20 kg MP ha-1 and 10 t organic fertilizers ha-1. We measured concentrations of MP in the soil, microbiological indicators of C cycling (microbial biomass and enzyme activities), and crop yields over 1.5 years. Background concentration of MP in the top 10 cm was 296 ± 110 (mean ± standard error) particles <0.5 mm per kg soil, with polypropylene, polystyrene, and polyethylene as the main polymers. Added LDPE and PLA/PBAT particles showed no changes in number and particle size over time. MP did not affect the soil microbiological indicators of C cycling or crop yields. Numerous MP occur in arable soils, suggesting diffuse MP entry into soils. In addition to conventional MP, biodegradable MP may persist under field conditions. However, MP at current concentrations are not expected to affect C turnover and crop yield.Ministry of Environment, Climate and Energy of Baden-WürttembergDeutsche ForschungsgemeinschaftProjekt DEA

Global wheat production with 1.5 and 2.0°C above pre‐industrial warming

Efforts to limit global warming to below 2°C in relation to the pre‐industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre‐industrial period) on global wheat production and local yield variability. A multi‐crop and multi‐climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by −2.3% to 7.0% under the 1.5°C scenario and −2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980–2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter‐annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer—India, which supplies more than 14% of global wheat. The projected global impact of warming <2°C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

The chaos in calibrating crop models

Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of system models and has an important impact on simulated values. Here we propose and illustrate a novel method of developing guidelines for calibration of system models. Our example is calibration of the phenology component of crop models. The approach is based on a multi-model study, where all teams are provided with the same data and asked to return simulations for the same conditions. All teams are asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices.HighlightsWe propose a new approach to deriving calibration recommendations for system modelsApproach is based on analyzing calibration in multi-model simulation exercisesResulting recommendations are holistic and anchored in actual practiceWe apply the approach to calibration of crop models used to simulate phenologyRecommendations concern: objective function, parameters to estimate, software usedCompeting Interest StatementThe authors have declared no competing interest

Predictability and stability testing to assess clinical decision instrument performance for children after blunt torso trauma

Objective The Pediatric Emergency Care Applied Research Network (PECARN) has developed a clinical-decision instrument (CDI) to identify children at very low risk of intra-abdominal injury. However, the CDI has not been externally validated. We sought to vet the PECARN CDI with the Predictability Computability Stability (PCS) data science framework, potentially increasing its chance of a successful external validation. Materials & methods We performed a secondary analysis of two prospectively collected datasets: PECARN (12,044 children from 20 emergency departments) and an independent external validation dataset from the Pediatric Surgical Research Collaborative (PedSRC; 2,188 children from 14 emergency departments). We used PCS to reanalyze the original PECARN CDI along with new interpretable PCS CDIs developed using the PECARN dataset. External validation was then measured on the PedSRC dataset. Results Three predictor variables (abdominal wall trauma, Glasgow Coma Scale Score Conclusion The PCS data science framework vetted the PECARN CDI and its constituent predictor variables prior to external validation. We found that the 3 stable predictor variables represented all of the PECARN CDI’s predictive performance on independent external validation. The PCS framework offers a less resource-intensive method than prospective validation to vet CDIs before external validation. We also found that the PECARN CDI will generalize well to new populations and should be prospectively externally validated. The PCS framework offers a potential strategy to increase the chance of a successful (costly) prospective validation. Author summary Do predictability and stability testing inform how a clinical decision instrument for identifying children at low risk of intra-abdominal injuries undergoing intervention after blunt torso trauma will perform prior to external validation? The PECARN instrument has high prediction performance and stable predictor variables. The Predictability, Computability, Stability (PCS) framework identified high performing instruments after development but before external validation. The PECARN instrument has high predictability and stability for children after blunt torso trauma and should therefore undergo prospective external validation. PCS is an effective method for evaluating clinical decision instruments after development but prior to external validation