Do early-life exposures explain why more advantaged children get eczema? Findings from the U.K. Millennium Cohort Study

Background: Atopic dermatitis (eczema) in childhood is socially patterned, with higher incidence in more advantaged populations. However, it is unclear what factors explain the social differences. Objectives: To identify early-life risk factors for eczema, and to explore how early-life risk factors explain any differences in eczema. Methods: We estimated odds ratios (ORs) for ever having had eczema by age 5 years in 14 499 children from the U.K. Millennium Cohort Study (MCS), with a focus on maternal, antenatal and early-life risk factors and socioeconomic circumstances (SECs). Risk factors were explored to assess whether they attenuated associations between SECs and eczema. Results: Overall 35·1% of children had ever had eczema by age 5 years. Children of mothers with degree-level qualifications vs. no educational qualifications were more likely to have eczema (OR 1·52, 95% confidence interval 1·31–1·76), and there was a gradient across the socioeconomic spectrum. Maternal atopy, breastfeeding (1–6 weeks and ≥ 6 months), introduction of solids under 4 months or cow's milk under 9 months, antibiotic exposure in the first year of life and grime exposure were associated with an increased odds of having eczema. Female sex, Pakistani and Bangladeshi ethnicity, smoking during pregnancy, exposure to environmental tobacco smoke and having more siblings were associated with reduced odds for eczema. Controlling for maternal, antenatal and early-life characteristics (particularly maternal smoking during pregnancy, breastfeeding and number of siblings) reduced the OR for eczema to 1·26 (95% confidence interval 1·03–1·50) in the group with the highest educational qualifications compared with the least. Conclusions: In a representative U.K. child cohort, eczema was more common in more advantaged children. This was explained partially by early-life factors including not smoking during pregnancy, breastfeeding and having fewer siblings

Can Mg isotopes be used to trace cyanobacteria-mediated magnesium carbonate precipitation in alkaline lakes?

The fractionation of Mg isotopes was determined during the cyanobacterial mediated precipitation of hydrous magnesium carbonate precipitation in both natural environments and in the laboratory. Natural samples were obtained from Lake Salda (SE Turkey), one of the few modern environments on the Earth's surface where hydrous Mg-carbonates are the dominant precipitating minerals. This precipitation was associated with cyanobacterial stromatolites which were abundant in this aquatic ecosystem. Mg isotope analyses were performed on samples of incoming streams, groundwaters, lake waters, stromatolites, and hydromagnesite-rich sediments. Laboratory Mg carbonate precipitation experiments were conducted in the presence of purified Synechococcus sp cyanobacteria that were isolated from the lake water and stromatolites. The hydrous magnesium carbonates nesquehonite (MgCO3·3H2O) and dypingite (Mg5(CO3)4(OH)25(H2O)) were precipitated in these batch reactor experiments from aqueous solutions containing either synthetic NaHCO3/MgCl2 mixtures or natural Lake Salda water, in the presence and absence of live photosynthesizing Synechococcus sp. Bulk precipitation rates were not to affected by the presence of bacteria when air was bubbled through the system. In the stirred non-bubbled reactors, conditions similar to natural settings, bacterial photosynthesis provoked nesquehonite precipitation, whilst no precipitation occurred in bacteria-free systems in the absence of air bubbling, despite the fluids achieving a similar or higher degree of supersaturation. The extent of Mg isotope fractionation (?26Mgsolid-solution) between the mineral and solution in the abiotic experiments was found to be identical, within uncertainty, to that measured in cyanobacteria-bearing experiments, and ranges from ?1.4 to ?0.7 ‰. This similarity refutes the use of Mg isotopes to validate microbial mediated precipitation of hydrous Mg carbonate

Using legume-based mixtures to enhance the nitrogen use efficiency and economic viability of cropping systems - Final report (LK09106/HGCA3447)

As costs for mineral fertilisers rise, legume-based leys are recognised as a potential alternative nitrogen source for crops. Here we demonstrate that including species-rich legume-based leys in rotations helps to maximise synergies between agricultural productivity and other ecosystem services. By using functionally diverse plant species mixtures, these services can be optimised and fine-tuned to regional and farm-specific needs. Replicated field experiments were conducted over three years at multiple locations, testing the performance of 12 legume species and 4 grass species sown in monocultures, as well as in a mixture of 10 of the legumes and all 4 grasses (called the All Species Mix, ASM). In addition, we compared this complex mixture to farmer-chosen ley mixtures on 34 sites across the UK. The trials showed that there is a large degree of functional complementarity among the legume species. No single species scored high on all evaluation criteria. In particular, the currently most frequently used species, white clover, is outscored by other legume species on a number of parameters such as early development and resistance to decomposition. Further complementarity emerged from the different responses of legume species to environmental variables, with soil pH and grazing or cutting regime being among the more important factors. For example, while large birdsfoot trefoil showed better performance on more acidic soils, the opposite was true for sainfoin, lucerne and black medic. In comparison with the monocultures, the ASM showed increased ground cover, increased above-ground biomass and reduced weed biomass. Benefits of mixing species with regard to productivity increased over time. In addition, the stability of biomass production across sites was greater in the ASM than in the legume monocultures. Within the on-farm trials, we further found that on soils low in organic matter the biomass advantage of the ASM over the Control ley was more marked than on the soils with higher organic matter content. Ecological modelling revealed that the three best multifunctional mixtures all contained black medic, lucerne and red clover. Within the long term New Farming Systems (NFS) rotational study, the use of a clover bi-crop showed improvement to soil characteristics compared to current practice (e.g. bulk density and water infiltration rate). Improvements in wheat yield were also noted with respect to the inclusion of a clover bi-crop in 2010, but there was evidence of a decline in response as the N dose was increased. Cumulatively, over both the wheat crop and the spring oilseed rape crop, the clover bi-crop improved margin over N. The highest average yield response (~9%) was associated with the ASM legume species mix cover cropping approach

The effect of radiative cooling on scaling laws of X-ray groups and clusters

We have performed cosmological simulations in a ΛCDM cosmology with and without radiative cooling in order to study the effect of cooling on the cluster scaling laws. Our simulations consist of 4.1 million particles each of gas and dark matter within a box size of 100 h-1 Mpc, and the run with cooling is the largest of its kind to have been evolved to z = 0. Our cluster catalogs both consist of over 400 objects and are complete in mass down to ~1013 h-1 M☉. We contrast the emission-weighted temperature-mass (Tew-M) and bolometric luminosity-temperature (Lbol-Tew) relations for the simulations at z = 0. We find that radiative cooling increases the temperature of intracluster gas and decreases its total luminosity, in agreement with the results of Pearce et al. Furthermore, the temperature dependence of these effects flattens the slope of the Tew-M relation and steepens the slope of the Lbol-Tew relation. Inclusion of radiative cooling in the simulations is sufficient to reproduce the observed X-ray scaling relations without requiring excessive nongravitational energy injection

Geometric Exponents, SLE and Logarithmic Minimal Models

In statistical mechanics, observables are usually related to local degrees of freedom such as the Q < 4 distinct states of the Q-state Potts models or the heights of the restricted solid-on-solid models. In the continuum scaling limit, these models are described by rational conformal field theories, namely the minimal models M(p,p') for suitable p, p'. More generally, as in stochastic Loewner evolution (SLE_kappa), one can consider observables related to nonlocal degrees of freedom such as paths or boundaries of clusters. This leads to fractal dimensions or geometric exponents related to values of conformal dimensions not found among the finite sets of values allowed by the rational minimal models. Working in the context of a loop gas with loop fugacity beta = -2 cos(4 pi/kappa), we use Monte Carlo simulations to measure the fractal dimensions of various geometric objects such as paths and the generalizations of cluster mass, cluster hull, external perimeter and red bonds. Specializing to the case where the SLE parameter kappa = 4p'/p is rational with p < p', we argue that the geometric exponents are related to conformal dimensions found in the infinitely extended Kac tables of the logarithmic minimal models LM(p,p'). These theories describe lattice systems with nonlocal degrees of freedom. We present results for critical dense polymers LM(1,2), critical percolation LM(2,3), the logarithmic Ising model LM(3,4), the logarithmic tricritical Ising model LM(4,5) as well as LM(3,5). Our results are compared with rigourous results from SLE_kappa, with predictions from theoretical physics and with other numerical experiments. Throughout, we emphasize the relationships between SLE_kappa, geometric exponents and the conformal dimensions of the underlying CFTs.Comment: Added reference

Technical Report: Observations and reanalyses data: comparison and trends in Southeast Asia

Reanalyses data sets, being temporally and spatially complete and available on six hourly timescales, are extremely convenient to use. Real observations represent the climate system with greater fidelity than reanalyses can, given that the latter are a complicated blend of observations and models via an assimilation scheme and rely heavily on the assimilation scheme where observations are absent. Knowing whether the reanalyses data reflects real data can be difficult to establish. In this part of the report, the observed data is compared with three reanalyses data sets for the SE Asia region. We use observations from SYNOP and METAR reports. SYNOP and METAR data are, in effect, observations taken at met stations and delivered to the Global Telecommunication System (GTS). Once in the GTS, they can be archived by institutions such as those delivering weather forecasts. Access to these data via the archives is generally much easier than through the individual Met Agencies. This is particularly true in the case of a study covering multiple nation states. These datasets are described in more detail in Sections 1.1 and 1.2

A longitudinal, observational study examining the relationships of patient satisfaction with services and mental well-being to their clinical course in young people with Type 1 diabetes mellitus during transition from child to adult health services

AIM: We hypothesized that participant well-being and satisfaction with services would be positively associated with a satisfactory clinical course during transition from child to adult health care. METHODS: Some 150 young people with Type 1 diabetes mellitus from five diabetes units in England were recruited to a longitudinal study of transition. Each young person was visited at home four times by a research assistant; each visit was 1 year apart. Satisfaction with services (Mind the Gap; MTG) and mental well-being (Warwick-Edinburgh Mental Well-being Scale; WEMWBS) were captured. Change in HbA1c , episodes of ketoacidosis, clinic and retinal screening attendance were used to assess clinical course. In total, 108 of 150 (72%) young people had sufficient data for analysis at visit 4. RESULTS: Mean age at entry was 16 years. By visit 4, 81.5% had left paediatric healthcare services. Median HbA1c increased significantly (P = 0.01) from 69 mmol/mol (8.5%) at baseline to 75 mmol/mol (9.0%) at visit 4. WEMWBS scores were comparable with those in the general population at baseline and were stable over the study period. MTG scores were also stable. By visit 4, some 32 individuals had a 'satisfactory' and 76 a 'suboptimal' clinical course. There were no significant differences in average WEMWBS and MTG scores between the clinical course groups (P = 0.96, 0.52 respectively); nor was there a significant difference in transfer status between the clinical course groups. CONCLUSIONS: The well-being of young people with diabetes and their satisfaction with transition services are not closely related to their clinical course. Investigating whether innovative psycho-educational interventions can improve the clinical course is a research priority

Simulation of associative learning with the replaced elements model

Associative learning theories can be categorised according to whether they treat the representation of stimulus compounds in an elemental or configural manner. Since it is clear that a simple elemental approach to stimulus representation is inadequate there have been several attempts to produce more elaborate elemental models. One recent approach, the Replaced Elements Model (Wagner, 2003), reproduces many results that have until recently been uniquely predicted by Pearce’s Configural Theory (Pearce, 1994). Although it is possible to simulate the Replaced Elements Model using “standard” simulation programs the generation of the correct stimulus representation is complex. The current paper describes a method for simulation of the Replaced Elements Model and presents the results of two example simulations that show differential predictions of Replaced Elements and Pearce’s Configural Theor

Mitigating Gender Bias in Machine Learning Data Sets

Artificial Intelligence has the capacity to amplify and perpetuate societal biases and presents profound ethical implications for society. Gender bias has been identified in the context of employment advertising and recruitment tools, due to their reliance on underlying language processing and recommendation algorithms. Attempts to address such issues have involved testing learned associations, integrating concepts of fairness to machine learning and performing more rigorous analysis of training data. Mitigating bias when algorithms are trained on textual data is particularly challenging given the complex way gender ideology is embedded in language. This paper proposes a framework for the identification of gender bias in training data for machine learning.The work draws upon gender theory and sociolinguistics to systematically indicate levels of bias in textual training data and associated neural word embedding models, thus highlighting pathways for both removing bias from training data and critically assessing its impact.Comment: 10 pages, 5 figures, 5 Tables, Presented as Bias2020 workshop (as part of the ECIR Conference) - http://bias.disim.univaq.i