Artificial drainage of peatlands: hydrological and hydrochemical process and wetland restoration

Peatlands have been subject to artificial drainage for centuries. This drainage has been in response to agricultural demand, forestry, horticultural and energy properties of peat and alleviation of flood risk. However, the are several environmental problems associated with drainage of peatlands. This paper describes the nature of these problems and examines the evidence for changes in hydrological and hydrochemical processes associated with these changes. Traditional black-box water balance approaches demonstrate little about wetland dynamics and therefore the science of catchment response to peat drainage is poorly understood. It is crucial that a more process-based approach be adopted within peatland ecosystems. The environmental problems associated with peat drainage have led, in part, to a recent reversal in attitudes to peatlands and we have seen a move towards wetland restoration. However, a detailed understanding of hydrological, hydrochemical and ecological process-interactions will be fundamental if we are to adequately restore degraded peatlands, preserve those that are still intact and understand the impacts of such management actions at the catchment scale

How to Educate Entrepreneurs?

Entrepreneurship education has two purposes: To improve students’ entrepreneurial skills and to provide impetus to those suited to entrepreneurship while discouraging the rest. While entrepreneurship education helps students to make a vocational decision its effects may conflict for those not suited to entrepreneurship. This study shows that vocational and the skill formation effects of entrepreneurship education can be identified empirically by drawing on the Theory of Planned Behavior. This is embedded in a structural equation model which we estimate and test using a robust 2SLS estimator. We find that the attitudinal factors posited by the Theory of Planned Behavior are positively correlated with students’ entrepreneurial intentions. While conflicting effects of vocational and skill directed course content are observed in some individuals, overall these types of content are complements. This finding contradicts previous results in the literature. We reconcile the conflicting findings and discuss implications for the design of entrepreneurship courses

A review of assessment methods for river hydromorphology

The work leading to this paper has received funding for the EU’s FP7 under Grant Agreement No. 282656 (REFORM

Understanding active school travel through the Behavioural Ecological Model

Active school travel (AST) is an important source of physical activity for children and a conceptual understanding of AST is necessary to inform promotion efforts. The aim of this article is to provide a conceptual analysis of AST. All currently identified AST formulations include intra-individual variables which are often recommended as intervention targets. However, existing literature lacks clarity on precisely how these intra-individual variables might shape specific AST interventions. Moreover, evaluative studies of AST interventions typically fail to specify an underpinning theory or model. To address this limitation, the Behavioural Ecological Model (BEM), not previously addressed in AST, is presented to guide this area of research. Based on specific examples, we draw attention to the role of potential antecedents and potential reinforcers of AST, as well as potential reinforcers of motorised travel. Antecedents and reinforcers may help to explain choices of school travel mode, and to inform and increase intervention options to promote AST. Consistent with the BEM, the provision of more immediate consequences, such as fun and material prizes, is an evidence-based strategy for increasing AST which is likely to be low-cost and easier to deliver than alternative interventions. This approach to the study of AST is expected to contribute to similar analyses in this and other areas of behaviour change research, and to a more useful discussion and treatment of theoretical and conceptual behavioural models

Evaluation of Jackknife and Bootstrap for Defining Confidence Intervals for Pairwise Agreement Measures

Several research fields frequently deal with the analysis of diverse classification results of the same entities. This should imply an objective detection of overlaps and divergences between the formed clusters. The congruence between classifications can be quantified by clustering agreement measures, including pairwise agreement measures. Several measures have been proposed and the importance of obtaining confidence intervals for the point estimate in the comparison of these measures has been highlighted. A broad range of methods can be used for the estimation of confidence intervals. However, evidence is lacking about what are the appropriate methods for the calculation of confidence intervals for most clustering agreement measures. Here we evaluate the resampling techniques of bootstrap and jackknife for the calculation of the confidence intervals for clustering agreement measures. Contrary to what has been shown for some statistics, simulations showed that the jackknife performs better than the bootstrap at accurately estimating confidence intervals for pairwise agreement measures, especially when the agreement between partitions is low. The coverage of the jackknife confidence interval is robust to changes in cluster number and cluster size distribution

Measuring Distributional Effects of Fiscal Reforms

The purpose of this paper is to provide an overview of how to analyse the distributional effects of fiscal reforms. Thereby, distributional effects shall be differentiated by four subconcepts, i.e. 1.) the traditional concept of inequality, 2.) the rather novel concept of polarisation, 3.) the concept of progression in taxation, and 4.) the concepts of income poverty and richness. The concept of inequality and the concept of income poverty are the by far most widely applied concepts in empirical analyses, probably since they appear to be the most transparent ones in their structure as well as the most controversial ones in political affairs. However, the concepts of richness, polarisation and progression in taxation shall additionally be subject of this analysis, since they appear to be useful devices on the course of analysing cause and effect of the other two concepts

Prolonged immune alteration following resolution of acute inflammation in humans.

Acute inflammation is an immediate response to infection and injury characterised by the influx of granulocytes followed by phagocytosing mononuclear phagocytes. Provided the antigen is cleared and the immune system of the host is fully functional, the acute inflammatory response will resolve. Until now it is considered that resolution then leads back to homeostasis, the physiological state tissues experienced before inflammation occurred. Using a human model of acute inflammation driven by intradermal UV killed Escherichia coli, we found that bacteria and granulocyte clearance as well as pro-inflammatory cytokine catabolism occurred by 72h. However, following a lag phase of about 4 days there was an increase in numbers of memory T cells and CD163+ macrophage at the post-resolution site up to day 17 as well as increased biosynthesis of cyclooxygenase-derived prostanoids and DHA-derived D series resolvins. Inhibiting post-resolution prostanoids using naproxen showed that numbers of tissue memory CD4 cells were under the endogenous control of PGE2, which exerts its suppressive effects on T cell proliferation via the EP4 receptor. In addition, we re-challenged the post-resolution site with a second injection of E. coli, which when compared to saline controls resulted in primarily a macrophage-driven response with comparatively fewer PMNs; the macrophage-dominated response was reversed by cyclooxygenase inhibition. Re-challenge experiments were also carried out in mice where we obtained similar results as in humans. Therefore, we report that acute inflammatory responses in both humans and rodents do not revert back to homeostasis, but trigger a hitherto unappreciated sequence of immunological events that dictate subsequent immune response to infection.Wellcome Trust Senior Research Fellowship (Grant number: WT087520), Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant number: 107613/Z/15/Z) and the Barts Charity (Grant number: MGU0343)

Predicting risk for Alcohol Use Disorder using longitudinal data with multimodal biomarkers and family history: a machine learning study.

Predictive models have succeeded in distinguishing between individuals with Alcohol use Disorder (AUD) and controls. However, predictive models identifying who is prone to develop AUD and the biomarkers indicating a predisposition to AUD are still unclear. Our sample (n = 656) included offspring and non-offspring of European American (EA) and African American (AA) ancestry from the Collaborative Study of the Genetics of Alcoholism (COGA) who were recruited as early as age 12 and were unaffected at first assessment and reassessed years later as AUD (DSM-5) (n = 328) or unaffected (n = 328). Machine learning analysis was performed for 220 EEG measures, 149 alcohol-related single nucleotide polymorphisms (SNPs) from a recent large Genome-wide Association Study (GWAS) of alcohol use/misuse and two family history (mother DSM-5 AUD and father DSM-5 AUD) features using supervised, Linear Support Vector Machine (SVM) classifier to test which features assessed before developing AUD predict those who go on to develop AUD. Age, gender, and ancestry stratified analyses were performed. Results indicate significant and higher accuracy rates for the AA compared with the EA prediction models and a higher model accuracy trend among females compared with males for both ancestries. Combined EEG and SNP features model outperformed models based on only EEG features or only SNP features for both EA and AA samples. This multidimensional superiority was confirmed in a follow-up analysis in the AA age groups (12-15, 16-19, 20-30) and EA age group (16-19). In both ancestry samples, the youngest age group achieved higher accuracy score than the two other older age groups. Maternal AUD increased the model's accuracy in both ancestries' samples. Several discriminative EEG measures and SNPs features were identified, including lower posterior gamma, higher slow wave connectivity (delta, theta, alpha), higher frontal gamma ratio, higher beta correlation in the parietal area, and 5 SNPs: rs4780836, rs2605140, rs11690265, rs692854, and rs13380649. Results highlight the significance of sampling uniformity followed by stratified (e.g., ancestry, gender, developmental period) analysis, and wider selection of features, to generate better prediction scores allowing a more accurate estimation of AUD development

Search for π⁰ decays to invisible particles

The NA62 experiment at the CERN SPS reports a study of a sample of 4 × 109 tagged π0 mesons from K+ → π+π0(γ), searching for the decay of the π0 to invisible particles. No signal is observed in excess of the expected background fluctuations. An upper limit of 4.4 × 10−9 is set on the branching ratio at 90% confidence level, improving on previous results by a factor of 60. This result can also be interpreted as a model- independent upper limit on the branching ratio for the decay K+ → π+X, where X is a particle escaping detection with mass in the range 0.110–0.155 GeV/c2 and rest lifetime greater than 100 ps. Model-dependent upper limits are obtained assuming X to be an axion-like particle with dominant fermion couplings or a dark scalar mixing with the Standard Model Higgs boson