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Rationale: Characterization of patterns of wheezing and allergic sensitization in early life may allow for identification of specific environmental exposures impacting asthma development.Objectives: To define respiratory phenotypes in inner-city children and their associations with early-life environmental exposures.Methods: Data were collected prospectively from 442 children in the URECA (Urban Environment and Childhood Asthma) birth cohort through age 7 years, reflecting symptoms (wheezing), aeroallergen sensitization, pulmonary function, and body mass index. Latent class mixed models identified trajectories of wheezing, allergic sensitization, and pulmonary function. Cluster analysis defined nonoverlapping groups (termed phenotypes). Potential associations between phenotypes and early-life environmental exposures were examined.Measurements and Main Results: Five phenotypes were identified and mainly differentiated by patterns of wheezing and allergic sensitization (low wheeze/low atopy; low wheeze/high atopy; transient wheeze/low atopy; high wheeze/low atopy; high wheeze/high atopy). Asthma was most often present in the high-wheeze phenotypes, with greatest respiratory morbidity among children with frequent wheezing and allergic sensitization. These phenotypes differentially related to early-life exposures, including maternal stress and depression, antenatal environmental tobacco smoke, house dust microbiome, and allergen content (all P < 0.05). Prenatal smoke exposure, maternal stress, and depression were highest in the high-wheeze/low-atopy phenotype. The high-wheeze/high-atopy phenotype was associated with low household microbial richness and diversity. Early-life aeroallergen exposure was low in high-wheeze phenotypes.Conclusions: Patterns of wheezing, allergic sensitization, and lung function identified five respiratory phenotypes among inner-city children. Early-life environmental exposure to stress, depression, tobacco smoke, and indoor allergens and microbes differentially associate with specific phenotypes. [ABSTRACT FROM AUTHOR]

Asthma in the inner-city population is usually atopic in nature, and is associated with significant morbidity and mortality. However, the underlying immune abnormalities that underlie asthma in urban adults have not been well defined. We investigated the influence of atopy and asthma on cytokine responses of inner-city adult women to define immune abnormalities associated with asthma and atopy. Blood samples were collected from 509 of 606 inner-city women enrolled in the Urban Environment and Childhood Asthma (URECA) study. We tested for associations between atopy and asthma status and cytokine responses in peripheral blood mononuclear cells incubated ex vivo with a panel of innate and adaptive immune stimulants. Atopic subjects had heightened Th2 cytokine responses (IL-4, IL-5, IL-13) to cockroach and dust mite antigens, tetanus toxoid, and phytohemagglutinin ( P < 0.05 for all). Differences in cytokine responses were greatest in response to stimulation with cockroach and dust mite. In a multivariate analysis, atopy was broadly related to increased Th2-like responses to all antigens and PHA, while asthma was only weakly related to mitogen-induced IL-4 and IL-5 responses. There were few asthma or allergy-related differences in responses to innate stimuli, including IFN-α and IFN-γ responses. In this inner-city adult female population, atopy is associated with enhanced Th2 responses to allergens and other stimuli, and there was little or no additional signal attributable to asthma. In particular, these data indicate that altered systemic interferon and innate immune responses are not associated with allergies and/or asthma in inner-city women. [ABSTRACT FROM AUTHOR]

This work presents a novel system based on artificial immune system for spam detection. A relatively new machine learning method inspired by the human immune system called Artificial Immune System (AIS) has been emerging recently. This method is currently undergoing intense investigation and demonstration. Core modifications were applied on the standard AIS with the aid of the Genetic Algorithm (GA). SpamAssassin corpus is used in all our simulations. Spam is a serious universal problem which causes problems for almost all computer users. This issue affects not only normal users of the internet, but also causes problems for companies and organizations due to expensive costs in lost productivity, wasting users' time and network bandwidth. Many studies on spam indicate that it costs organizations billions of dollars annually. We introduce a GA assisted AIS in spam detection, and compare between two methods. Encouraging results were achieved when comparing to commercially available anti-spam software. [ABSTRACT FROM AUTHOR]