Immunological resilience and biodiversity for prevention of allergic diseases and asthma

Increase of allergic conditions has occurred at the same pace with the Great Acceleration, which stands for the rapid growth rate of human activities upon earth from 1950s. Changes of environment and lifestyle along with escalating urbanization are acknowledged as the main underlying causes. Secondary (tertiary) prevention for better disease control has advanced considerably with innovations for oral immunotherapy and effective treatment of inflammation with corticosteroids, calcineurin inhibitors, and biological medications. Patients are less disabled than before. However, primary prevention has remained a dilemma. Factors predicting allergy and asthma risk have proven complex: Risk factors increase the risk, while protective factors counteract them. Interaction of human body with environmental biodiversity with micro-organisms and biogenic compounds as well as the central role of epigenetic adaptation in immune homeostasis have given new insight. Allergic diseases are good indicators of the twisted relation to environment. In various non-communicable diseases, the protective mode of the immune system indicates low-grade inflammation without apparent cause. Giving microbes, pro- and prebiotics, has shown some promise in prevention and treatment. The real-world public health programme in Finland (2008-2018) emphasized nature relatedness and protective factors for immunological resilience, instead of avoidance. The nationwide action mitigated the allergy burden, but in the lack of controls, primary preventive effect remains to be proven. The first results of controlled biodiversity interventions are promising. In the fast urbanizing world, new approaches are called for allergy prevention, which also has a major cost saving potential.Peer reviewe

Contingency table techniques for three dimensional atom probe tomography.

A contingency table analysis procedure is developed and applied to three dimensional atom probe data sets for the investigation of fine-scale solute co-/anti-segregation effects in multicomponent alloys. Potential sources of error and inaccuracy are identified and eliminated from the technique. The conventional P value testing techniques associated with chi(2) are shown to be unsatisfactory and can become ambiguous in cases of large block numbers or high solute concentrations. The coefficient of contingency is demonstrated to be an acceptable and useful basis of comparison for contingency table analyses of differently-conditioned materials. However, care must be taken in choice of block size and to maintain a consistent overall composition between experiments. The coefficient is dependent upon block size and solute composition, and cannot be used to compare analyses with significantly different solute compositions or to assess the extent of clustering without reference to that of the randomly ordered case. It is shown that as clustering evolves into larger precipitates and phases, contingency table analysis becomes inappropriate. Random labeling techniques are introduced to infer further meaning from the coefficient of contingency. We propose the comparison of experimental result, mu(exp), to the randomized value, micro(rand), as a new method by which to interpret the quantity of solute clustering present in a material. It is demonstrated that how this method may be utilized to identify an appropriate size of contingency table analysis blocks into which the data set is partitioned to optimize the significance of the results

New techniques for the analysis of fine-scaled clustering phenomena within atom probe tomography (APT) data.

Nanoscale atomic clusters in atom probe tomographic data are not universally defined but instead are characterized by the clustering algorithm used and the parameter values controlling the algorithmic process. A new core-linkage clustering algorithm is developed, combining fundamental elements of the conventional maximum separation method with density-based analyses. A key improvement to the algorithm is the independence of algorithmic parameters inherently unified in previous techniques, enabling a more accurate analysis to be applied across a wider range of material systems. Further, an objective procedure for the selection of parameters based on approximating the data with a model of complete spatial randomness is developed and applied. The use of higher nearest neighbor distributions is highlighted to give insight into the nature of the clustering phenomena present in a system and to generalize the clustering algorithms used to analyze it. Maximum separation, density-based scanning, and the core linkage algorithm, developed within this study, were separately applied to the investigation of fine solute clustering of solute atoms in an Al-1.9Zn-1.7Mg (at.%) at two distinct states of early phase decomposition and the results of these analyses were evaluated