How Exposure to Environmental Tobacco Smoke, Outdoor Air Pollutants, and Increased Pollen Burdens Influences the Incidence of Asthma

Asthma is a multifactorial airway disease that arises from a relatively common genetic background interphased with exposures to allergens and airborne irritants. The rapid rise in asthma over the past three decades in Western societies has been attributed to numerous diverse factors, including increased awareness of the disease, altered lifestyle and activity patterns, and ill-defined changes in environmental exposures. It is well accepted that persons with asthma are more sensitive than persons without asthma to air pollutants such as cigarette smoke, traffic emissions, and photochemical smog components. It has also been demonstrated that exposure to a mix of allergens and irritants can at times promote the development phase (induction) of the disease. Experimental evidence suggests that complex organic molecules from diesel exhaust may act as allergic adjuvants through the production of oxidative stress in airway cells. It also seems that climate change is increasing the abundance of aeroallergens such as pollen, which may result in greater incidence or severity of allergic diseases. In this review we illustrate how environmental tobacco smoke, outdoor air pollution, and climate change may act as environmental risk factors for the development of asthma and provide mechanistic explanations for how some of these effects can occur

Planetary Nebulae as standard candles XI. Application to Spiral Galaxies

We report the results of an [O III] lambda 5007 survey for planetary nebulae (PN) in three spiral galaxies: M101 (NGC 5457), M51 (NGC 5194/5195) and M96 (NGC 3368). By comparing on-band/off-band [O III] lambda 5007 images with images taken in H-alpha and broadband R, we identify 65, 64 and 74 PN candidates in each galaxy, respectively. From these data, an adopted M31 distance of 770 kpc, and the empirical planetary nebula luminosity function (PNLF), we derive distances to M101, M51, and M96 of 7.7 +/- 0.5, 8.4 +/- 0.6, and 9.6 +/- 0.6 Mpc. These observations demonstrate that the PNLF technique can be successfully applied to late-type galaxies, and provide an important overlap between the Population I and Population II distance scales. We also discuss some special problems associated with using the PNLF in spiral galaxies, including the effects of dust and the possible presence of [O III] bright supernova remnants.Comment: 38 pages, TeX, with tables included but not figures. Uses epsf.tex and kpnobasic.tex. To be published in the Astophysical Journal. Full paper is available at http://www.astro.psu.edu/users/johnf/Text/research.htm

Fundamental movement skills are more than run, throw and catch: The role of stability skills.

Introduction In motor development literature fundamental movement skills are divided into three constructs: locomotive, object control and stability skills. Most fundamental movement skills research has focused on children's competency in locomotor and object control skills. The first aim of this study was to validate a test battery to assess the construct of stability skills, in children aged 6 to 10 (M age = 8.2, SD = 1.2). Secondly we assessed how the stability skills construct fitted into a model of fundamental movement skill. Method The Delphi method was used to select the stability skill battery. Confirmatory factor analysis (CFA) was used to assess if the skills loaded onto the same construct and a new model of FMS was developed using structural equation modelling. Results Three postural control tasks were selected (the log roll, rock and back support) because they had good face and content validity. These skills also demonstrated good predictive validity with gymnasts scoring significantly better than children without gymnastic training and children from a high SES school performing better than those from a mid and low SES schools and the mid SES children scored better than the low SES children (all p < .05). Inter rater reliability tests were excellent for all three skills (ICC = 0.81, 0.87, 0.87) as was test retest reliability (ICC 0.87-0.95). CFA provided good construct validity, and structural equation modelling revealed stability skills to be an independent factor in an overall FMS model which included locomotor (r = .88), object control (r = .76) and stability skills (r = .81). Discussion This study provides a rationale for the inclusion of stability skills in FMS assessment. The stability skills could be used alongside other FMS assessment tools to provide a holistic assessment of children's fundamental movement skills. Copyright

The impact of rheumatoid foot on disability in Colombian patients with rheumatoid arthritis

<p>Abstract</p> <p>Background</p> <p>Alterations in the feet of patients with rheumatoid arthritis (RA) are a cause of disability in this population. The purpose of this research was to evaluate the impact that foot impairment has on the patients' global quality of life (QOL) based on validated scales and its relationship to disease activity.</p> <p>Methods</p> <p>This was a cross-sectional study in which 95 patients with RA were enrolled. A complete physical examination, including a full foot assessment, was done. The Spanish versions of the Health Assessment Questionnaire (HAQ) Disability Index and of the Disease Activity Score (DAS 28) were administered. A logistic regression model was used to analyze data and obtain adjusted odds ratios (AORs).</p> <p>Results</p> <p>Foot deformities were observed in 78 (82%) of the patients; hallux valgus (65%), medial longitudinal arch flattening (42%), claw toe (lesser toes) (39%), dorsiflexion restriction (tibiotalar) (34%), cock-up toe (lesser toes) (25%), and transverse arch flattening (25%) were the most frequent. In the logistic regression analysis (adjusted for age, gender and duration of disease), forefoot movement pain, subtalar movement pain, tibiotalar movement pain and plantarflexion restriction (tibiotalar) were strongly associated with disease activity and disability. The positive squeeze test was significantly associated with disability risk (AOR = 6,3; 95% CI, 1.28–30.96; P = 0,02); hallux valgus, and dorsiflexion restriction (tibiotalar) were associated with disease activity.</p> <p>Conclusion</p> <p>Foot abnormalities are associated with active joint disease and disability in RA. Foot examinations provide complementary information related to the disability as an indirect measurement of quality of life and activity of disease in daily practice.</p

Identification of novel DNA repair proteins via primary sequence, secondary structure, and homology

<p>Abstract</p> <p>Background</p> <p>DNA repair is the general term for the collection of critical mechanisms which repair many forms of DNA damage such as methylation or ionizing radiation. DNA repair has mainly been studied in experimental and clinical situations, and relatively few information-based approaches to new extracting DNA repair knowledge exist. As a first step, automatic detection of DNA repair proteins in genomes via informatics techniques is desirable; however, there are many forms of DNA repair and it is not a straightforward process to identify and classify repair proteins with a single optimal method. We perform a study of the ability of homology and machine learning-based methods to identify and classify DNA repair proteins, as well as scan vertebrate genomes for the presence of novel repair proteins. Combinations of primary sequence polypeptide frequency, secondary structure, and homology information are used as feature information for input to a Support Vector Machine (SVM).</p> <p>Results</p> <p>We identify that SVM techniques are capable of identifying portions of DNA repair protein datasets without admitting false positives; at low levels of false positive tolerance, homology can also identify and classify proteins with good performance. Secondary structure information provides improved performance compared to using primary structure alone. Furthermore, we observe that machine learning methods incorporating homology information perform best when data is filtered by some clustering technique. Analysis by applying these methodologies to the scanning of multiple vertebrate genomes confirms a positive correlation between the size of a genome and the number of DNA repair protein transcripts it is likely to contain, and simultaneously suggests that all organisms have a non-zero minimum number of repair genes. In addition, the scan result clusters several organisms' repair abilities in an evolutionarily consistent fashion. Analysis also identifies several functionally unconfirmed proteins that are highly likely to be involved in the repair process. A new web service, INTREPED, has been made available for the immediate search and annotation of DNA repair proteins in newly sequenced genomes.</p> <p>Conclusion</p> <p>Despite complexity due to a multitude of repair pathways, combinations of sequence, structure, and homology with Support Vector Machines offer good methods in addition to existing homology searches for DNA repair protein identification and functional annotation. Most importantly, this study has uncovered relationships between the size of a genome and a genome's available repair repetoire, and offers a number of new predictions as well as a prediction service, both which reduce the search time and cost for novel repair genes and proteins.</p

Generative Embedding for Model-Based Classification of fMRI Data

Decoding models, such as those underlying multivariate classification algorithms, have been increasingly used to infer cognitive or clinical brain states from measures of brain activity obtained by functional magnetic resonance imaging (fMRI). The practicality of current classifiers, however, is restricted by two major challenges. First, due to the high data dimensionality and low sample size, algorithms struggle to separate informative from uninformative features, resulting in poor generalization performance. Second, popular discriminative methods such as support vector machines (SVMs) rarely afford mechanistic interpretability. In this paper, we address these issues by proposing a novel generative-embedding approach that incorporates neurobiologically interpretable generative models into discriminative classifiers. Our approach extends previous work on trial-by-trial classification for electrophysiological recordings to subject-by-subject classification for fMRI and offers two key advantages over conventional methods: it may provide more accurate predictions by exploiting discriminative information encoded in ‘hidden’ physiological quantities such as synaptic connection strengths; and it affords mechanistic interpretability of clinical classifications. Here, we introduce generative embedding for fMRI using a combination of dynamic causal models (DCMs) and SVMs. We propose a general procedure of DCM-based generative embedding for subject-wise classification, provide a concrete implementation, and suggest good-practice guidelines for unbiased application of generative embedding in the context of fMRI. We illustrate the utility of our approach by a clinical example in which we classify moderately aphasic patients and healthy controls using a DCM of thalamo-temporal regions during speech processing. Generative embedding achieves a near-perfect balanced classification accuracy of 98% and significantly outperforms conventional activation-based and correlation-based methods. This example demonstrates how disease states can be detected with very high accuracy and, at the same time, be interpreted mechanistically in terms of abnormalities in connectivity. We envisage that future applications of generative embedding may provide crucial advances in dissecting spectrum disorders into physiologically more well-defined subgroups