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Soil pollutants such as hydrocarbons can induce toxic effects in plants and associated arbuscular mycorrhizal fungi (AMF). This study was conducted to evaluate if the legume Lotus corniculatus and the grass Elymus trachycaulus and arbuscular mycorrhizal fungi could grow in two oil sands processing by-products after bitumen extraction from the oil sands in northern Alberta, Canada. Substrate treatments were coarse tailings sand (CTS), a mix of dry mature fine tailings (MFT) with CTS (1: 1) and Pleistocene sandy soil (hydrocarbon free); microbial treatments were without AMF, with AMF and AMF plus soil bacteria isolated from oil sands reclamation sites. Plant biomass, root morphology, leaf water content, shoot tissue phosphorus content and mycorrhizal colonization were evaluated. Both plant species had reduced growth in CTS and tailings mix relative to sandy soil. AMF frequency and intensity in roots of E. trachycaulus was not influenced by soil hydrocarbons; however, it decreased significantly over time in roots of L. corniculatus without bacteria in CTS. Mycorrhizal inoculation alone did not significantly improve plant growth in CTS and tailings mix; however, inoculation with mycorrhizae plus bacteria led to a significantly positive response of both plant species in CTS. Thus, combined inoculation with selected mycorrhizae and bacteria led to synergistic effects. Such combinations may be used in future to improve plant growth in reclamation of CTS and tailings mix.
Malnutrition is widespread in older people and represents a major geriatric syndrome with multifactorial etiology and severe consequences for health outcomes and quality of life. The aim of the present paper is to describe current approaches and evidence regarding malnutrition treatment and to highlight relevant knowledge gaps that need to be addressed. Recently published guidelines of the European Society for Clinical Nutrition and Metabolism (ESPEN) provide a summary of the available evidence and highlight the wide range of different measures that can be taken—from the identification and elimination of potential causes to enteral and parenteral nutrition—depending on the patient’s abilities and needs. However, more than half of the recommendations therein are based on expert consensus because of a lack of evidence, and only three are concern patient-centred outcomes. Future research should further clarify the etiology of malnutrition and identify the most relevant causes in order to prevent malnutrition. Based on limited and partly conflicting evidence and the limitations of existing studies, it remains unclear which interventions are most effective in which patient groups, and if specific situations, diseases or etiologies of malnutrition require specific approaches. Patient-relevant outcomes such as functionality and quality of life need more attention, and research methodology should be harmonised to allow for the comparability of studies.
Over the last two decades, macroecology the analysis of large-scale, multi-species ecological patterns and processes has established itself as a major line of biological research. Analyses of statistical links between environmental variables and biotic responses have long and successfully been employed as a main approach, but new developments are due to be utilized. Scanning the horizon of macroecology, we identified four challenges that will probably play a major role in the future. We support our claims by examples and bibliographic analyses. 1) Integrating the past into macroecological analyses, e.g. by using paleontological or phylogenetic information or by applying methods from historical biogeography, will sharpen our understanding of the underlying reasons for contemporary patterns. 2) Explicit consideration of the local processes that lead to the observed larger-scale patterns is necessary to understand the fine-grain variability found in nature, and will enable better prediction of future patterns (e.g. under environmental change conditions). 3) Macroecology is dependent on large-scale, high quality data from a broad spectrum of taxa and regions. More available data sources need to be tapped and new, small-grain large-extent data need to be collected. 4) Although macroecology already lead to mainstreaming cutting-edge statistical analysis techniques, we find that more sophisticated methods are needed to account for the biases inherent to sampling at large scale. Bayesian methods may be particularly suitable to address these challenges. To continue the vigorous development of the macroecological research agenda, it is time to address these challenges and to avoid becoming too complacent with current achievements.
The majority of cases of community-acquired pneumonia are caused by Streptococcus pneumoniae and most studies on pneumococcal host interaction are based on cell culture or animal experiments. Thus, little is known about infections in human lung tissue.
Cyclooxygenase-2 and its metabolites play an important regulatory role in lung inflammation. Therefore, we established a pneumococcal infection model on human lung tissue demonstrating mitogen-activated protein kinase (MAPK)-dependent induction of cyclooxygenase-2 and its related metabolites.
In addition to alveolar macrophages and the vascular endothelium, cyclooxygenase-2 was upregulated in alveolar type II but not type I epithelial cells, which was confirmed in lungs of patients suffering from acute pneumonia. Moreover, we demonstrated the expression profile of all four E prostanoid receptors at the mRNA level and showed functionality of the E prostanoid(4) receptor by cyclic adenosine monophosphate production. Additionally, in comparison to previous studies, cyclooxygenase-2/prostaglandin E-2 related pro- and anti-inflammatory mediator regulation was partly confirmed in human lung tissue after pneumococcal infection.
Overall, cell type-specific and MAPK-dependent cyclooxygenase-2 expression and prostaglandin E-2 formation in human lung tissue may play an important role in the early phase of pneumococcal infections.
Population-level effects of global warming result from concurrent direct and indirect processes. They are typically described by physiologically structured population models (PSPMs). Therefore, inverse modelling offers a tool to identify parameters of individual physiological processes through population-level data analysis, e. g. the temperature dependence of growth from size-frequency data of a field population. Here, we make use of experiments under laboratory conditions, in mesocosms and field monitoring to determine the temperature dependence of growth and mortality of Gammarus pulex. We found an optimum temperature for growth of approximately 17 degrees C and a related temperature coefficient, Q(10), of 1.5 degrees C(-1), irrespective of whether we classically fitted individual growth curves or applied inverse modelling based on PSPMs to laboratory data. From a comparison of underlying data sets we conclude that applying inverse modelling techniques to population-level data results in meaningful response parameters for physiological processes if additional temperature-driven effects, including within-population interaction, can be excluded or determined independently. If this is not the case, parameter estimates describe a cumulative response, e. g. comprising temperature-dependent resource dynamics. Finally, fluctuating temperatures in natural habitats increased the uncertainty in parameter values. Here, PSPM should be applied for virtual monitoring in order to determine a sampling scheme that comprises important dates to reduce parameter uncertainty.
Perspectives in modelling earthworm dynamics and their feedbacks with abiotic soil properties
(2012)
Effects of earthworms on soil abiotic properties are well documented from several decades of laboratory and mesocosm experiments, and they are supposed to affect large-scale soil ecosystem functioning. The prediction of the spatiotemporal occurrence of earthworms and the related functional effects in the field or at larger scales, however, is constrained by adequate modelling approaches. Correlative, phenomenological methods, such as species distribution models, facilitate the identification of factors that drive species' distributions. However, these methods ignore the ability of earthworms to select and modify their own habitat and therefore may lead to unreliable predictions. Understanding these feedbacks between earthworms and abiotic soil properties is a key requisite to better understand their spatiotemporal distribution as well as to quantify the various functional effects of earthworms in soil ecosystems. Process-based models that investigate either effects or responses of earthworms on soil environmental conditions are mostly applied in ecotoxicological and bioturbation studies. Process-based models that describe feedbacks between earthworms and soil abiotic properties explicitly are rare. In this review, we analysed 18 process-based earthworm dynamic modelling studies pointing out the current gaps and future challenges in feedback modelling. We identify three main challenges: (i) adequate and reliable process identification in model development at and across relevant spatiotemporal scales (individual behaviour and population dynamics of earthworms), (ii) use of information from different data sources in one model (laboratory or field experiments, earthworm species or functional type) and (iii) quantification of uncertainties in data (e.g. spatiotemporal variability of earthworm abundances and soil hydraulic properties) and derived parameters (e.g. population growth rate and hydraulic conductivity) that are used in the model.
Introduction: Chronic low back pain (LBP) is a major cause of disability; early diagnosis and stratification of care remain challenges.
Objectives: This article describes the development of a screening tool for the 1-year prognosis of patients with high chronic LBP risk (risk stratification index) and for treatment allocation according to treatment-modifiable yellow flag indicators (risk prevention indices, RPI-S).
Methods: Screening tools were derived from a multicentre longitudinal study (n = 1071, age >18, intermittent LBP). The greatest prognostic predictors of 4 flag domains ("pain," "distress," "social-environment," "medical care-environment") were determined using least absolute shrinkage and selection operator regression analysis. Internal validity and prognosis error were evaluated after 1-year follow-up. Receiver operating characteristic curves for discrimination (area under the curve) and cutoff values were determined.
Results: The risk stratification index identified persons with increased risk of chronic LBP and accurately estimated expected pain intensity and disability on the Pain Grade Questionnaire (0-100 points) up to 1 year later with an average prognosis error of 15 points. In addition, 3-risk classes were discerned with an accuracy of area under the curve = 0.74 (95% confidence interval 0.63-0.85). The RPI-S also distinguished persons with potentially modifiable prognostic indicators from 4 flag domains and stratified allocation to biopsychosocial treatments accordingly.
Conclusion: The screening tools, developed in compliance with the PROGRESS and TRIPOD statements, revealed good validation and prognostic strength. These tools improve on existing screening tools because of their utility for secondary preventions, incorporation of exercise effect modifiers, exact pain estimations, and personalized allocation to multimodal treatments.
Introduction: Chronic low back pain (LBP) is a major cause of disability; early diagnosis and stratification of care remain challenges.
Objectives: This article describes the development of a screening tool for the 1-year prognosis of patients with high chronic LBP risk (risk stratification index) and for treatment allocation according to treatment-modifiable yellow flag indicators (risk prevention indices, RPI-S).
Methods: Screening tools were derived from a multicentre longitudinal study (n = 1071, age >18, intermittent LBP). The greatest prognostic predictors of 4 flag domains ("pain," "distress," "social-environment," "medical care-environment") were determined using least absolute shrinkage and selection operator regression analysis. Internal validity and prognosis error were evaluated after 1-year follow-up. Receiver operating characteristic curves for discrimination (area under the curve) and cutoff values were determined.
Results: The risk stratification index identified persons with increased risk of chronic LBP and accurately estimated expected pain intensity and disability on the Pain Grade Questionnaire (0-100 points) up to 1 year later with an average prognosis error of 15 points. In addition, 3-risk classes were discerned with an accuracy of area under the curve = 0.74 (95% confidence interval 0.63-0.85). The RPI-S also distinguished persons with potentially modifiable prognostic indicators from 4 flag domains and stratified allocation to biopsychosocial treatments accordingly.
Conclusion: The screening tools, developed in compliance with the PROGRESS and TRIPOD statements, revealed good validation and prognostic strength. These tools improve on existing screening tools because of their utility for secondary preventions, incorporation of exercise effect modifiers, exact pain estimations, and personalized allocation to multimodal treatments.