Risk factors for long-term mortality after acute aortic dissection-results of the German registry for acute aortic dissection type a long-term follow-up.

OBJECTIVES: Several short-term analyses from German Registry for Acute Aortic Dissection Type A (GERAADA) have been published. This study investigated whether short-term risk factors are transferable to the long-term prognosis of patients. METHODS: Thirty-three centres with 2686 patients participated in the long-term follow-up. A total of 1164 patients died, 1063 survived and 459 were lost to follow-up during the follow-up timeframe (mean duration: 10.2 years). Long-term mortality of the cohort was compared with an age-stratified, German population. RESULTS: One, 5 and 10 years after initial surgery, the survival of the GERAADA patient cohort was 71.4%, 63.4% and 51%, respectively. Without the early deaths (90-day mortality 25.4%), survival was calculated after 1, 5 and 10 years: 95.6%, 83.5% and 68.3%. Higher age, longer extracorporeal circulation time, shorter perioperative ventilation time and postoperative neurologic deficits were predictive of long-term prognosis. In an age-divided landmark analysis, the mortality of aortic dissection surgery survivors was found to be similar to that of the general German population. If patients are sorted in risk groups according to the GERAADA score, long-term survival differs between the risk groups. CONCLUSIONS: If patients have survived an acute postoperative period of 90 days, life expectancy comparable to that of the general German population can be assumed in lower- and medium-risk patients. Whether the GERAADA score can provide valuable insights into the long-term prognosis of patients undergoing surgery for acute aortic dissection type A is still unclear.

Benchmarking inference methods for water quality monitoring and status classification.

River water quality monitoring at limited temporal resolution can lead to imprecise and inaccurate classification of physicochemical status due to sampling error. Bayesian inference allows for the quantification of this uncertainty, which can assist decision-making. However, implicit assumptions of Bayesian methods can cause further uncertainty in the uncertainty quantification, so-called second-order uncertainty. In this study, and for the first time, we rigorously assessed this second-order uncertainty for inference of common water quality statistics (mean and 95th percentile) based on sub-sampling high-frequency (hourly) total reactive phosphorus (TRP) concentration data from three watersheds. The statistics were inferred with the low-resolution sub-samples using the Bayesian lognormal distribution and bootstrap, frequentist t test, and face-value approach and were compared with those of the high-frequency data as benchmarks. The t test exhibited a high risk of bias in estimating the water quality statistics of interest and corresponding physicochemical status (up to 99% of sub-samples). The Bayesian lognormal model provided a good fit to the high-frequency TRP concentration data and the least biased classification of physicochemical status (< 5% of sub-samples). Our results suggest wide applicability of Bayesian inference for water quality status classification, a new approach for regulatory practice that provides uncertainty information about water quality monitoring and regulatory classification with reduced bias compared to frequentist approaches. Furthermore, the study elucidates sizeable second-order uncertainty due to the choice of statistical model, which could be quantified based on the high-frequency data.

Crystal structures of three cyclohexane-based γ-spirolactams: determination of configurations and conformations.

The title compounds, 2-azaspiro[4.5]deca-1-one, C9H15NO, (1a), cis-8-methyl-2-azaspiro[4.5]deca-1-one, C10H17NO, (1b), and trans-8-methyl-2-azaspiro[4.5]deca-1-one, C10H17NO, (1c), were synthesized from benzoic acids 2 in only 3 steps in high yields. Crystallization from n-hexane afforded single crystals, suitable for X-ray diffraction. Thus, the configurations, conformations, and interesting crystal packing effects have been determined unequivocally. The bicyclic skeleton consists of a lactam ring, attached by a spiro junction to a cyclohexane ring. The lactam ring adopts an envelope conformation and the cyclohexane ring has a chair conformation. The main difference between compound 1b and compound 1c is the position of the carbonyl group on the 2-pyrrolidine ring with respect to the methyl group on the 8-position of the cyclohexane ring, which is cis (1b) or trans (1c). A remarkable feature of all three compounds is the existence of a mirror plane within the molecule. Given that all compounds crystallize in centrosymmetric space groups, the packing always contains interesting enantiomer-like pairs. Finally, the structures are stabilized by intermolecular N-H···O hydrogen bonds.

Bayesian inference of uncertainty in freshwater quality caused by low-resolution monitoring.

Regulatory, low temporal resolution monitoring of freshwater quality does not fully capture the frequency distributions of the requisite parameters, particularly those that are highly skewed and heavy-tailed. Hence the summary statistics ultimately compared to environmental standards are uncertain. Quantifying this uncertainty is crucial for robust water quality assessment and possible remediation, but requires strong assumptions. This paper compares three ways to model the missing data needed to fully characterise a frequency distribution in a Bayesian framework using multi-year/multi-location orthophosphate (arithmetic mean standard), dissolved oxygen (DO; 10th percentile standard) and ammonia (90th percentile standard) data from the Tamar catchment in Southwest England. First, fitting an assumed parametric model of the frequency distribution (lognormal or Weibull), there is appreciable uncertainty around the "best" model fit. Second, Bayesian Model Averaging is more general in accommodating cases where the data are ambiguous with regard to the best model, but does not take into account possibly missing data. Third, a quasi-nonparametric multinomial model of the monitoring process that places some weight on those missing data yields wider and heavier-tailed frequency distributions. One-at-a-time sensitivity analysis suggests that the multinomial model for mean orthophosphate is sensitive to the choice of support range and the prior weights given to the missing data. Sensitivity is lower for 10th percentile DO and 90th percentile ammonia. The resultant probability densities of ecological status under the EU Water Framework Directive span several status classes, meaning ecological status is more uncertain than previously acknowledged. For orthophosphate, the regulatory, empirical determination of ecological status is not only overly precise but also biased.

A transdisciplinary account of water research.

Water research is introduced from the combined perspectives of natural and social science and cases of citizen and stakeholder coproduction of knowledge. Using the overarching notion of transdisciplinarity, we examine how interdisciplinary and participatory water research has taken place and could be developed further. It becomes apparent that water knowledge is produced widely within society, across certified disciplinary experts and noncertified expert stakeholders and citizens. However, understanding and management interventions may remain partial, or even conflicting, as much research across and between traditional disciplines has failed to integrate disciplinary paradigms due to philosophical, methodological, and communication barriers. We argue for more agonistic relationships that challenge both certified and noncertified knowledge productively. These should include examination of how water research itself embeds and is embedded in social context and performs political work. While case studies of the cultural and political economy of water knowledge exist, we need more empirical evidence on how exactly culture, politics, and economics have shaped this knowledge and how and at what junctures this could have turned out differently. We may thus channel the coproductionist critique productively to bring perspectives, alternative knowledges, and implications into water politics where they were not previously considered; in an attempt to counter potential lock-in to particular water policies and technologies that may be inequitable, unsustainable, or unacceptable. While engaging explicitly with politics, transdisciplinary water research should remain attentive to closing down moments in the research process, such as framings, path-dependencies, vested interests, researchers' positionalities, power, and scale. WIREs Water 2016, 3:369-389. doi: 10.1002/wat2.1132 For further resources related to this article, please visit the WIREs website.

Contrasting controls on the phosphorus concentration of suspended particulate matter under baseflow and storm event conditions in agricultural headwater streams.

Whilst the processes involved in the cycling of dissolved phosphorus (P) in rivers have been extensively studied, less is known about the mechanisms controlling particulate P concentrations during small and large flows. This deficiency is addressed through an analysis of large numbers of suspended particulate matter (SPM) samples collected under baseflow (n=222) and storm event (n=721) conditions over a 23-month period across three agricultural headwater catchments of the River Wensum, UK. Relationships between clay mineral and metal oxyhydroxide associated elements were assessed and multiple linear regression models for the prediction of SPM P concentration under baseflow and storm event conditions were formulated. These models, which explained 71-96% of the variation in SPM P concentration, revealed a pronounced shift in P association from iron (Fe) dominated during baseflow conditions to particulate organic carbon (POC) dominated during storm events. It is hypothesised this pronounced transition in P control mechanism, which is consistent across the three study catchments, is driven by changes in SPM source area under differing hydrological conditions. In particular, changes in SPM Fe-P ratios between small and large flows suggest there are three distinct sources of SPM Fe; surface soils, subsurface sediments and streambed iron sulphide. Further examination of weekly baseflow data also revealed seasonality in the Fe-P and aluminium oxalate-dithionate (Alox-Aldi) ratios of SPM, indicating temporal variability in sediment P sorption capacity. The results presented here significantly enhance our understanding of SPM P associations with soil derived organic and inorganic fractions under different flow regimes and has implications for the mitigation of P originating from different sources in agricultural catchments.

Apportioning sources of organic matter in streambed sediments: an integrated molecular and compound-specific stable isotope approach.

We present a novel application for quantitatively apportioning sources of organic matter in streambed sediments via a coupled molecular and compound-specific isotope analysis (CSIA) of long-chain leaf wax n-alkane biomarkers using a Bayesian mixing model. Leaf wax extracts of 13 plant species were collected from across two environments (aquatic and terrestrial) and four plant functional types (trees, herbaceous perennials, and C3 and C4 graminoids) from the agricultural River Wensum catchment, UK. Seven isotopic (δ13C27, δ13C29, δ13C31, δ13C27-31, δ2H27, δ2H29, and δ2H27-29) and two n-alkane ratio (average chain length (ACL), carbon preference index (CPI)) fingerprints were derived, which successfully differentiated 93% of individual plant specimens by plant functional type. The δ2H values were the strongest discriminators of plants originating from different functional groups, with trees (δ2H27-29=-208‰ to -164‰) and C3 graminoids (δ2H27-29=-259‰ to -221‰) providing the largest contrasts. The δ13C values provided strong discrimination between C3 (δ13C27-31=-37.5‰ to -33.8‰) and C4 (δ13C27-31=-23.5‰ to -23.1‰) plants, but neither δ13C nor δ2H values could uniquely differentiate aquatic and terrestrial species, emphasizing a stronger plant physiological/biochemical rather than environmental control over isotopic differences. ACL and CPI complemented isotopic discrimination, with significantly longer chain lengths recorded for trees and terrestrial plants compared with herbaceous perennials and aquatic species, respectively. Application of a comprehensive Bayesian mixing model for 18 streambed sediments collected between September 2013 and March 2014 revealed considerable temporal variability in the apportionment of organic matter sources. Median organic matter contributions ranged from 22% to 52% for trees, 29% to 50% for herbaceous perennials, 17% to 34% for C3 graminoids and 3% to 7% for C4 graminoids. The results presented here clearly demonstrate the effectiveness of an integrated molecular and stable isotope analysis for quantitatively apportioning, with uncertainty, plant-specific organic matter contributions to streambed sediments via a Bayesian mixing model approach.

Sensitivity of fluvial sediment source apportionment to mixing model assumptions: A Bayesian model comparison.

Mixing models have become increasingly common tools for apportioning fluvial sediment load to various sediment sources across catchments using a wide variety of Bayesian and frequentist modeling approaches. In this study, we demonstrate how different model setups can impact upon resulting source apportionment estimates in a Bayesian framework via a one-factor-at-a-time (OFAT) sensitivity analysis. We formulate 13 versions of a mixing model, each with different error assumptions and model structural choices, and apply them to sediment geochemistry data from the River Blackwater, Norfolk, UK, to apportion suspended particulate matter (SPM) contributions from three sources (arable topsoils, road verges, and subsurface material) under base flow conditions between August 2012 and August 2013. Whilst all 13 models estimate subsurface sources to be the largest contributor of SPM (median ∼76%), comparison of apportionment estimates reveal varying degrees of sensitivity to changing priors, inclusion of covariance terms, incorporation of time-variant distributions, and methods of proportion characterization. We also demonstrate differences in apportionment results between a full and an empirical Bayesian setup, and between a Bayesian and a frequentist optimization approach. This OFAT sensitivity analysis reveals that mixing model structural choices and error assumptions can significantly impact upon sediment source apportionment results, with estimated median contributions in this study varying by up to 21% between model versions. Users of mixing models are therefore strongly advised to carefully consider and justify their choice of model structure prior to conducting sediment source apportionment investigations. KEY POINTS: An OFAT sensitivity analysis of sediment fingerprinting mixing models is conductedBayesian models display high sensitivity to error assumptions and structural choicesSource apportionment results differ between Bayesian and frequentist approaches.

Uncertainties in data and models to describe event dynamics of agricultural sediment and phosphorus transfer.

Mathematical models help to quantify agricultural sediment and phosphorus transfers and to simulate mitigation of pollution. This paper develops empirical models of the dominant sediment and phosphorus event dynamics observed at high resolution in a drained and undrained, intensive grassland field-scale lysimeter (1 ha) experiment. The uncertainties in model development and simulation are addressed using Generalized Likelihood Uncertainty Estimation. A comparison of suspended solids (SS) and total phosphorus (TP) samples with a limited number of manual repeats indicates larger data variability at low flows. Quantitative uncertainty estimates for discharge (Q) are available from another study. Suspended solids-discharge (SS-Q) hysteresis is analyzed for four events and two drained and two undrained fields. Hysteresis loops differ spatially and temporally, and exhaustion is apparent between sequential hydrograph peaks. A coherent empirical model framework for hysteresis, where SS is a function of Q and rate of change of Q, is proposed. This is evaluated taking the Q uncertainty into account, which can contribute substantially to the overall uncertainty of model simulations. The model simulates small hysteresis loops well but fails to simulate exhaustion of SS sources and flushing at the onset of events. Analysis of the TP-SS relationship reveals that most of the variability occurs at low flows, and a power-law relationship can explain the dominant behavior at higher flows, which is consistent across events, fields, and pathways. The need for further field experiments to test hypotheses of sediment mobilization and to quantify data uncertainties is identified.