The need for clean air: the way air pollution and climate change affect allergic rhinitis and asthma

Air pollution and climate change have a significant impact on human health and well‐being and contribute to the onset and aggravation of allergic rhinitis and asthma among other chronic respiratory diseases. In Westernized countries, households have experienced a process of increasing insulation and individuals tend to spend most of their time indoors. These sequelae implicate a high exposure to indoor allergens (house dust mites, pets, molds, etc.), tobacco smoke and other pollutants, which have an impact on respiratory health. Outdoor air pollution derived from traffic and other human activities not only has a direct negative effect on human health but also enhances the allergenicity of some plants and contributes to global warming. Climate change modifies the availability and distribution of plant‐ and fungal‐derived allergens and increases the frequency of extreme climate events. This review summarizes the effects of indoor air pollution, outdoor air pollution and subsequent climate change on asthma and allergic rhinitis in children and adults and addresses the policy adjustments and lifestyle changes required to mitigate their deleterious effects

Using Graph Components Derived from an Associative Concept Dictionary to Predict fMRI Neural Activation Patterns that Represent the Meaning of Nouns

In this study, we introduce an original distance definition for graphs, called the Markov-inverse-F measure (MiF). This measure enables the integration of classical graph theory indices with new knowledge pertaining to structural feature extraction from semantic networks. MiF improves the conventional Jaccard and/or Simpson indices, and reconciles both the geodesic information (random walk) and co-occurrence adjustment (degree balance and distribution). We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain. Specifically, the MiF distance is computed between each of the nouns used in a previous neural experiment and each of the in-between words in a subgraph derived from the Edinburgh Word Association Thesaurus of English. From the MiF-based information matrix, a machine learning model can accurately obtain a scalar parameter that specifies the degree to which each voxel in (the MRI image of) the brain is activated by each word or each principal component of the intermediate semantic features. Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created. This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations