Green areas around homes reduce atopic sensitization in children

Peer reviewe

Defect detection in textile fabric images using subband domain subspace analysis

In this work, a new model that combines the concepts of wavelet transformation and subspace analysis tools, like Independent Component Analysis, Topographic Independent Component Analysis, and Independent Subspace Analysis, is developed for the purpose of defect detection in textile images. In previous works, it has been shown that reduction of the textural components of the textile image by preprocessing has increased the performance of the system. Based on this observation, in present work, the aforementioned subspace analysis tools are aimed to be applied on the sub-band images. The feature vector of a sub-window of a test image is compared with that of the defect-free image in order to make a decision. This decision is based on a Euclidean distance classifier. The performance increase that results using wavelet transformation prior to subspace analysis has been discussed in detail. While all the subspace analysis methods has been found to lead to the same detection performances, as a further step, independent subspace analysis is used to classify the detected defects according to their directionalities

DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model

Structural equation models and Bayesian networks have been widely used to analyze causal relations between continuous variables. In such frameworks, linear acyclic models are typically used to model the data-generating process of variables. Recently, it was shown that use of non-Gaussianity identifies the full structure of a linear acyclic model, i.e., a causal ordering of variables and their connection strengths, without using any prior knowledge on the network structure, which is not the case with conventional methods. However, existing estimation methods are based on iterative search algorithms and may not converge to a correct solution in a finite number of steps. In this paper, we propose a new direct method to estimate a causal ordering and connection strengths based on non-Gaussianity. In contrast to the previous methods, our algorithm requires no algorithmic parameters and is guaranteed to converge to the right solution within a small fixed number of steps if the data strictly follows the model

Latent physiological factors of complex human diseases revealed by independent component analysis of clinarrays

<p>Abstract</p> <p>Background</p> <p>Diagnosis and treatment of patients in the clinical setting is often driven by known symptomatic factors that distinguish one particular condition from another. Treatment based on noticeable symptoms, however, is limited to the types of clinical biomarkers collected, and is prone to overlooking dysfunctions in physiological factors not easily evident to medical practitioners. We used a vector-based representation of patient clinical biomarkers, or clinarrays, to search for latent physiological factors that underlie human diseases directly from clinical laboratory data. Knowledge of these factors could be used to improve assessment of disease severity and help to refine strategies for diagnosis and monitoring disease progression.</p> <p>Results</p> <p>Applying Independent Component Analysis on clinarrays built from patient laboratory measurements revealed both known and novel concomitant physiological factors for asthma, types 1 and 2 diabetes, cystic fibrosis, and Duchenne muscular dystrophy. Serum sodium was found to be the most significant factor for both type 1 and type 2 diabetes, and was also significant in asthma. TSH3, a measure of thyroid function, and blood urea nitrogen, indicative of kidney function, were factors unique to type 1 diabetes respective to type 2 diabetes. Platelet count was significant across all the diseases analyzed.</p> <p>Conclusions</p> <p>The results demonstrate that large-scale analyses of clinical biomarkers using unsupervised methods can offer novel insights into the pathophysiological basis of human disease, and suggest novel clinical utility of established laboratory measurements.</p

A comprehensive pharmacogenomic study indicates roles for SLCO1B1, ABCG2 and SLCO2B1 in rosuvastatin pharmacokinetics

AimsThe aim was to comprehensively investigate the effects of genetic variability on the pharmacokinetics of rosuvastatin.MethodsWe conducted a genome-wide association study and candidate gene analyses of single dose rosuvastatin pharmacokinetics in a prospective study (n = 159) and a cohort of previously published studies (n = 88).ResultsIn a genome-wide association meta-analysis of the prospective study and the cohort of previously published studies, the SLCO1B1 c.521 T > C (rs4149056) single nucleotide variation (SNV) associated with increased area under the plasma concentration–time curve (AUC) and peak plasma concentration of rosuvastatin (P = 1.8 × 10−12 and P = 3.2 × 10−15). The candidate gene analysis suggested that the ABCG2 c.421C > A (rs2231142) SNV associates with increased rosuvastatin AUC (P = .0079), while the SLCO1B1 c.388A > G (rs2306283) and SLCO2B1 c.1457C > T (rs2306168) SNVs associate with decreased rosuvastatin AUC (P = .0041 and P = .0076). Based on SLCO1B1 genotypes, we stratified the participants into poor, decreased, normal, increased and highly increased organic anion transporting polypeptide (OATP) 1B1 function groups. The OATP1B1 poor function phenotype associated with 2.1-fold (90% confidence interval 1.6–2.8, P = 4.69 × 10−5) increased AUC of rosuvastatin, whereas the OATP1B1 highly increased function phenotype associated with a 44% (16–62%; P = .019) decreased rosuvastatin AUC. The ABCG2 c.421A/A genotype associated with 2.2-fold (1.5–3.0; P = 2.6 × 10−4) increased AUC of rosuvastatin. The SLCO2B1 c.1457C/T genotype associated with 28% decreased rosuvastatin AUC (11–42%; P = .01).ConclusionThese data suggest roles for SLCO1B1, ABCG2 and SLCO2B1 in rosuvastatin pharmacokinetics. Poor SLCO1B1 or ABCG2 function genotypes may increase the risk of rosuvastatin-induced myotoxicity. Reduced doses of rosuvastatin are advisable for patients with these genotypes.</p

Emergence of Visual Saliency from Natural Scenes via Context-Mediated Probability Distributions Coding

Visual saliency is the perceptual quality that makes some items in visual scenes stand out from their immediate contexts. Visual saliency plays important roles in natural vision in that saliency can direct eye movements, deploy attention, and facilitate tasks like object detection and scene understanding. A central unsolved issue is: What features should be encoded in the early visual cortex for detecting salient features in natural scenes? To explore this important issue, we propose a hypothesis that visual saliency is based on efficient encoding of the probability distributions (PDs) of visual variables in specific contexts in natural scenes, referred to as context-mediated PDs in natural scenes. In this concept, computational units in the model of the early visual system do not act as feature detectors but rather as estimators of the context-mediated PDs of a full range of visual variables in natural scenes, which directly give rise to a measure of visual saliency of any input stimulus. To test this hypothesis, we developed a model of the context-mediated PDs in natural scenes using a modified algorithm for independent component analysis (ICA) and derived a measure of visual saliency based on these PDs estimated from a set of natural scenes. We demonstrated that visual saliency based on the context-mediated PDs in natural scenes effectively predicts human gaze in free-viewing of both static and dynamic natural scenes. This study suggests that the computation based on the context-mediated PDs of visual variables in natural scenes may underlie the neural mechanism in the early visual cortex for detecting salient features in natural scenes

Effect of lutein and antioxidant dietary supplementation on contrast sensitivity in age-related macular disease:A randomized controlled trial

Objective: The aim of the study is to determine the effect of lutein combined with vitamin and mineral supplementation on contrast sensitivity in people with age-related macular disease (ARMD). Design: A prospective, 9-month, double-masked randomized controlled trial. Setting: Aston University, Birmingham, UK and a UK optometric clinical practice. Subjects: Age-related maculopathy (ARM) and atrophic age-related macular degeneration (AMD) participants were randomized (using a random number generator) to either placebo (n = 10) or active (n=15) groups. Three of the placebo group and two of the active group dropped out. Interventions: The active group supplemented daily with 6 mg lutein combined with vitamins and minerals. The outcome measure was contrast sensitivity (CS) measured using the Pelli-Robson chart, for which the study had 80% power at the 5% significance level to detect a change of 0.3log units. Results: The CS score increased by 0.07 ± 0.07 and decreased by 0.02 ± 0.18 log units for the placebo and active groups, respectively. The difference between these values is not statistically significant (z = 0.903, P = 0.376). Conclusion: The results suggest that 6 mg of lutein supplementation in combination with other antioxidants is not beneficial for this group. Further work is required to establish optimum dosage levels