Incidence of first stroke and ethnic differences in stroke pattern in Bradford, UK: Bradford Stroke Study

Background: Information on ethnic disparities in stroke between White and Pakistani population in Europe is scarce. Bradford District has the largest proportion of Pakistani people in England; this provides a unique opportunity to study the difference in stroke between the two major ethnic groups. Aim: To determine the first-ever-stroke incidence and examine the disparities in stroke patterns between Whites and Pakistanis in Bradford. Methods: Prospective 12 months study consisting of 273,327 adults (≥18 years) residents. Stroke cases were identified by multiple overlapping approaches. Results: In the study period, 541 first-ever-strokes were recorded. The crude incidence rate was 198 per 100,000 person-years. Age adjusted-standardized rate to the World Health Organization world population of first-ever-stroke is 155 and 101 per 100,000 person-years in Pakistanis and Whites respectively. Four hundred and thirty-eight patients (81%) were Whites, 83 (15.3%) were Pakistanis, 11 (2%) were Indian and Bangladeshis, and 9 (1.7%) were of other ethnic origin. Pakistanis were significantly younger and had more obesity (p = 0.049), and diabetes mellitus (DM) (p = <0.001). They were less likely to suffer from atrial fibrillation (p = <0.001), be ex- or current smokers (p = <0.001), and drink alcohol above the recommended level (p = 0.007) compared with Whites. In comparison with Whites, higher rates of age-adjusted stroke (1.5-fold), lacunar infarction (threefold), and ischemic infarction due to large artery disease (twofold) were found in the Pakistanis. Conclusions: The incidence of first-ever-stroke is higher in the Pakistanis compared with the Whites in Bradford, UK. Etiology and vascular risk factors vary between the ethnic groups. This information should be considered when investigating stroke etiology, and when planning prevention and care provision to improve outcomes after stroke

Inactivation of Poxviruses by Upper-Room UVC Light in a Simulated Hospital Room Environment

In the event of a smallpox outbreak due to bioterrorism, delays in vaccination programs may lead to significant secondary transmission. In the early phases of such an outbreak, transmission of smallpox will take place especially in locations where infected persons may congregate, such as hospital emergency rooms. Air disinfection using upper-room 254 nm (UVC) light can lower the airborne concentrations of infective viruses in the lower part of the room, and thereby control the spread of airborne infections among room occupants without exposing occupants to a significant amount of UVC. Using vaccinia virus aerosols as a surrogate for smallpox we report on the effectiveness of air disinfection, via upper-room UVC light, under simulated real world conditions including the effects of convection, mechanical mixing, temperature and relative humidity. In decay experiments, upper-room UVC fixtures used with mixing by a conventional ceiling fan produced decreases in airborne virus concentrations that would require additional ventilation of more than 87 air changes per hour. Under steady state conditions the effective air changes per hour associated with upper-room UVC ranged from 18 to 1000. The surprisingly high end of the observed range resulted from the extreme susceptibility of vaccinia virus to UVC at low relative humidity and use of 4 UVC fixtures in a small room with efficient air mixing. Increasing the number of UVC fixtures or mechanical ventilation rates resulted in greater fractional reduction in virus aerosol and UVC effectiveness was higher in winter compared to summer for each scenario tested. These data demonstrate that upper-room UVC has the potential to greatly reduce exposure to susceptible viral aerosols. The greater survival at baseline and greater UVC susceptibility of vaccinia under winter conditions suggest that while risk from an aerosol attack with smallpox would be greatest in winter, protective measures using UVC may also be most efficient at this time. These data may also be relevant to influenza, which also has improved aerosol survival at low RH and somewhat similar sensitivity to UVC

Bidirectional best hit r-window gene clusters

<p>Abstract</p> <p>Background</p> <p><it>Conserved gene clusters </it>are groups of genes that are located close to one another in the genomes of several species. They tend to code for proteins that have a functional interaction. The identification of conserved gene clusters is an important step towards understanding genome evolution and predicting gene function.</p> <p>Results</p> <p>In this paper, we propose a novel pairwise gene cluster model that combines the notion of bidirectional best hits with the <it>r</it>-window model introduced in 2003 by Durand and Sankoff. The bidirectional best hit (BBH) constraint removes the need to specify the minimum number of shared genes in the <it>r</it>-window model and improves the relevance of the results. We design a subquadratic time algorithm to compute the set of BBH <it>r</it>-window gene clusters efficiently.</p> <p>Conclusion</p> <p>We apply our cluster model to the comparative analysis of <it>E. coli </it>K-12 and <it>B. subtilis </it>and perform an extensive comparison between our new model and the gene teams model developed by Bergeron <it>et al</it>. As compared to the gene teams model, our new cluster model has a slightly lower recall but a higher precision at all levels of recall when the results were ranked using statistical tests. An analysis of the most significant BBH <it>r</it>-window gene cluster show that they correspond to known operons.</p

Improving cluster recovery with feature rescaling factors

The data preprocessing stage is crucial in clustering. Features may describe entities using different scales. To rectify this, one usually applies feature normalisation aiming at rescaling features so that none of them overpowers the others in the objective function of the selected clustering algorithm. In this paper, we argue that the rescaling procedure should not treat all features identically. Instead, it should favour the features that are more meaningful for clustering. With this in mind, we introduce a feature rescaling method that takes into account the within-cluster degree of relevance of each feature. Our comprehensive simulation study, carried out on real and synthetic data, with and without noise features, clearly demonstrates that clustering methods that use the proposed data normalization strategy clearly outperform those that use traditional data normalization

Higher Doses of Subcutaneous IgG Reduce Resource Utilization in Patients with Primary Immunodeficiency

The recommended dose of IgG in primary immunodeficiency (PID) has been increasing since its first use. This study aimed to determine if higher subcutaneous IgG doses resulted in improved patient outcomes by comparing results from two parallel clinical studies with similar design. One patient cohort received subcutaneous IgG doses that were 1.5 times higher than their previous intravenous doses (mean 213 mg/kg/week), whereas the other cohort received doses identical to previous subcutaneous or intravenous doses (mean 120 mg/kg/week). While neither cohort had any serious infections, the cohort maintained on higher mean IgG dose had significantly lower rates of non-serious infections (2.76 vs. 5.18 episodes/year, P < 0.0001), hospitalization (0.20 vs. 3.48 days/year, P < 0.0001), antibiotic use (48.50 vs. 72.75 days/year, P < 0.001), and missed work/school activity (2.10 vs. 8.00 days/year, P < 0.001). The higher-dose cohort had lower health care utilization and improved indices of well being compared to the cohort treated with traditional IgG doses

Solitary waves in the Nonlinear Dirac Equation

In the present work, we consider the existence, stability, and dynamics of solitary waves in the nonlinear Dirac equation. We start by introducing the Soler model of self-interacting spinors, and discuss its localized waveforms in one, two, and three spatial dimensions and the equations they satisfy. We present the associated explicit solutions in one dimension and numerically obtain their analogues in higher dimensions. The stability is subsequently discussed from a theoretical perspective and then complemented with numerical computations. Finally, the dynamics of the solutions is explored and compared to its non-relativistic analogue, which is the nonlinear Schr{\"o}dinger equation. A few special topics are also explored, including the discrete variant of the nonlinear Dirac equation and its solitary wave properties, as well as the PT-symmetric variant of the model

Deep Sequencing the Transcriptome Reveals Seasonal Adaptive Mechanisms in a Hibernating Mammal

Mammalian hibernation is a complex phenotype involving metabolic rate reduction, bradycardia, profound hypothermia, and a reliance on stored fat that allows the animal to survive for months without food in a state of suspended animation. To determine the genes responsible for this phenotype in the thirteen-lined ground squirrel (Ictidomys tridecemlineatus) we used the Roche 454 platform to sequence mRNA isolated at six points throughout the year from three key tissues: heart, skeletal muscle, and white adipose tissue (WAT). Deep sequencing generated approximately 3.7 million cDNA reads from 18 samples (6 time points ×3 tissues) with a mean read length of 335 bases. Of these, 3,125,337 reads were assembled into 140,703 contigs. Approximately 90% of all sequences were matched to proteins in the human UniProt database. The total number of distinct human proteins matched by ground squirrel transcripts was 13,637 for heart, 12,496 for skeletal muscle, and 14,351 for WAT. Extensive mitochondrial RNA sequences enabled a novel approach of using the transcriptome to construct the complete mitochondrial genome for I. tridecemlineatus. Seasonal and activity-specific changes in mRNA levels that met our stringent false discovery rate cutoff (1.0×10−11) were used to identify patterns of gene expression involving various aspects of the hibernation phenotype. Among these patterns are differentially expressed genes encoding heart proteins AT1A1, NAC1 and RYR2 controlling ion transport required for contraction and relaxation at low body temperatures. Abundant RNAs in skeletal muscle coding ubiquitin pathway proteins ASB2, UBC and DDB1 peak in October, suggesting an increase in muscle proteolysis. Finally, genes in WAT that encode proteins involved in lipogenesis (ACOD, FABP4) are highly expressed in August, but gradually decline in expression during the seasonal transition to lipolysis

Hepatic Abnormalities Associated with Aluminum Loading in Piglets

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141476/1/jpen0293.pd

Turing learning: : A metric-free approach to inferring behavior and its application to swarms

We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. By observing the behavior of the system as well as the behaviors produced by the models, two sets of data samples are obtained. The classifiers are rewarded for discriminating between these two sets, that is, for correctly categorizing data samples as either genuine or counterfeit. Conversely, the models are rewarded for 'tricking' the classifiers into categorizing their data samples as genuine. Unlike other methods for system identification, Turing Learning does not require predefined metrics to quantify the difference between the system and its models. We present two case studies with swarms of simulated robots and prove that the underlying behaviors cannot be inferred by a metric-based system identification method. By contrast, Turing Learning infers the behaviors with high accuracy. It also produces a useful by-product - the classifiers - that can be used to detect abnormal behavior in the swarm. Moreover, we show that Turing Learning also successfully infers the behavior of physical robot swarms. The results show that collective behaviors can be directly inferred from motion trajectories of individuals in the swarm, which may have significant implications for the study of animal collectives. Furthermore, Turing Learning could prove useful whenever a behavior is not easily characterizable using metrics, making it suitable for a wide range of applications.Comment: camera-ready versio