Diversity of sympathetic vasoconstrictor pathways and their plasticity after spinal cord injury

Sympathetic vasoconstrictor pathways pass through paravertebral ganglia carrying ongoing and reflex activity arising within the central nervous system to their vascular targets. The pattern of reflex activity is selective for particular vascular beds and appropriate for the physiological outcome (vasoconstriction or vasodilation). The preganglionic signals are distributed to most postganglionic neurones in ganglia via synapses that are always suprathreshold for action potential initiation (like skeletal neuromuscular junctions). Most postganglionic neurones receive only one of these “strong” inputs, other preganglionic connections being ineffective. Pre- and postganglionic neurones discharge normally at frequencies of 0.5–1 Hz and maximally in short bursts at <10 Hz. Animal experiments have revealed unexpected changes in these pathways following spinal cord injury. (1) After destruction of preganglionic neurones or axons, surviving terminals in ganglia sprout and rapidly re-establish strong connections, probably even to inappropriate postganglionic neurones. This could explain aberrant reflexes after spinal cord injury. (2) Cutaneous (tail) and splanchnic (mesenteric) arteries taken from below a spinal transection show dramatically enhanced responses in vitro to norepinephrine released from perivascular nerves. However the mechanisms that are modified differ between the two vessels, being mostly postjunctional in the tail artery and mostly prejunctional in the mesenteric artery. The changes are mimicked when postganglionic neurones are silenced by removal of their preganglionic input. Whether or not other arteries are also hyperresponsive to reflex activation, these observations suggest that the greatest contribution to raised peripheral resistance in autonomic dysreflexia follows the modifications of neurovascular transmission

A computational framework to emulate the human perspective in flow cytometric data analysis

Background: In recent years, intense research efforts have focused on developing methods for automated flow cytometric data analysis. However, while designing such applications, little or no attention has been paid to the human perspective that is absolutely central to the manual gating process of identifying and characterizing cell populations. In particular, the assumption of many common techniques that cell populations could be modeled reliably with pre-specified distributions may not hold true in real-life samples, which can have populations of arbitrary shapes and considerable inter-sample variation. &lt;p/&gt;Results: To address this, we developed a new framework flowScape for emulating certain key aspects of the human perspective in analyzing flow data, which we implemented in multiple steps. First, flowScape begins with creating a mathematically rigorous map of the high-dimensional flow data landscape based on dense and sparse regions defined by relative concentrations of events around modes. In the second step, these modal clusters are connected with a global hierarchical structure. This representation allows flowScape to perform ridgeline analysis for both traversing the landscape and isolating cell populations at different levels of resolution. Finally, we extended manual gating with a new capacity for constructing templates that can identify target populations in terms of their relative parameters, as opposed to the more commonly used absolute or physical parameters. This allows flowScape to apply such templates in batch mode for detecting the corresponding populations in a flexible, sample-specific manner. We also demonstrated different applications of our framework to flow data analysis and show its superiority over other analytical methods. &lt;p/&gt;Conclusions: The human perspective, built on top of intuition and experience, is a very important component of flow cytometric data analysis. By emulating some of its approaches and extending these with automation and rigor, flowScape provides a flexible and robust framework for computational cytomics

Codominant scoring of AFLP in association panels

A study on the codominant scoring of AFLP markers in association panels without prior knowledge on genotype probabilities is described. Bands are scored codominantly by fitting normal mixture models to band intensities, illustrating and optimizing existing methodology, which employs the EM-algorithm. We study features that improve the performance of the algorithm, and the unmixing in general, like parameter initialization, restrictions on parameters, data transformation, and outlier removal. Parameter restrictions include equal component variances, equal or nearly equal distances between component means, and mixing probabilities according to Hardy–Weinberg Equilibrium. Histogram visualization of band intensities with superimposed normal densities, and optional classification scores and other grouping information, assists further in the codominant scoring. We find empirical evidence favoring the square root transformation of the band intensity, as was found in segregating populations. Our approach provides posterior genotype probabilities for marker loci. These probabilities can form the basis for association mapping and are more useful than the standard scoring categories A, H, B, C, D. They can also be used to calculate predictors for additive and dominance effects. Diagnostics for data quality of AFLP markers are described: preference for three-component mixture model, good separation between component means, and lack of singletons for the component with highest mean. Software has been developed in R, containing the models for normal mixtures with facilitating features, and visualizations. The methods are applied to an association panel in tomato, comprising 1,175 polymorphic markers on 94 tomato hybrids, as part of a larger study within the Dutch Centre for BioSystems Genomics

Random-phase approximation and its applications in computational chemistry and materials science

The random-phase approximation (RPA) as an approach for computing the electronic correlation energy is reviewed. After a brief account of its basic concept and historical development, the paper is devoted to the theoretical formulations of RPA, and its applications to realistic systems. With several illustrating applications, we discuss the implications of RPA for computational chemistry and materials science. The computational cost of RPA is also addressed which is critical for its widespread use in future applications. In addition, current correction schemes going beyond RPA and directions of further development will be discussed.Comment: 25 pages, 11 figures, published online in J. Mater. Sci. (2012

Thinking outside the curve, part I: modeling birthweight distribution

<p>Abstract</p> <p>Background</p> <p>Greater epidemiologic understanding of the relationships among fetal-infant mortality and its prognostic factors, including birthweight, could have vast public health implications. A key step toward that understanding is a realistic and tractable framework for analyzing birthweight distributions and fetal-infant mortality. The present paper is the first of a two-part series that introduces such a framework.</p> <p>Methods</p> <p>We propose describing a birthweight distribution via a normal mixture model in which the number of components is determined from the data using a model selection criterion rather than fixed <it>a priori</it>.</p> <p>Results</p> <p>We address a number of methodological issues, including how the number of components selected depends on the sample size, how the choice of model selection criterion influences the results, and how estimates of mixture model parameters based on multiple samples from the same population can be combined to produce confidence intervals. As an illustration, we find that a 4-component normal mixture model reasonably describes the birthweight distribution for a population of white singleton infants born to heavily smoking mothers. We also compare this 4-component normal mixture model to two competitors from the existing literature: a contaminated normal model and a 2-component normal mixture model. In a second illustration, we discover that a 6-component normal mixture model may be more appropriate than a 4-component normal mixture model for a general population of black singletons.</p> <p>Conclusions</p> <p>The framework developed in this paper avoids assuming the existence of an interval of birthweights over which there are no compromised pregnancies and does not constrain birthweights within compromised pregnancies to be normally distributed. Thus, the present framework can reveal heterogeneity in birthweight that is undetectable via a contaminated normal model or a 2-component normal mixture model.</p

CpG-free plasmids confer reduced inflammation and sustained pulmonary gene expression.

Pulmonary delivery of plasmid DNA (pDNA)/cationic liposome complexes is associated with an acute unmethylated CG dinucleotide (CpG)-mediated inflammatory response and brief duration of transgene expression. We demonstrate that retention of even a single CpG in pDNA is sufficient to elicit an inflammatory response, whereas CpG-free pDNA vectors do not. Using a CpG-free pDNA expression vector, we achieved sustained (≥56 d) in vivo transgene expression in the absence of lung inflammation

Determinants of male reproductive health disorders: the Men in Australia Telephone Survey (MATeS)

Background: The relationship between reproductive health disorders and lifestyle factors in middle-aged and older men is not clear. The aim of this study is to describe lifestyle and biomedical associations as possible causes of erectile dysfunction (ED), prostate disease (PD), lower urinary tract symptoms (LUTS) and perceived symptoms of androgen deficiency (pAD) in a representative population of middle-aged and older men, using the Men in Australia Telephone Survey (MATeS). Methods: A representative sample (n = 5990) of men aged 40+ years, stratified by age and State, was contacted by random selection of households, with an individual response rate of 78%. All men participated in a 20-minute computer-assisted telephone interview exploring general and reproductive health. Associations between male reproductive health disorders and lifestyle and biomedical factors were analysed using multivariate logistic regression (odds ratio [95% confidence interval]). Variables studied included age, body mass index, waist circumference, smoking, alcohol consumption, physical activity, co-morbid disease and medication use for hypertension, high cholesterol and symptoms of depression. Results: Controlling for age and a range of lifestyle and co-morbid exposures, sedentary lifestyle and being underweight was associated with an increased likelihood of ED (1.4 [1.1-1.8]; 2.9 [1.5-5.8], respectively) and pAD (1.3 [1.1-1.7]; 2.7 [1.4-5.0], respectively. Diabetes and cardiovascular disease were both associated with ED, with hypertension strongly associated with LUTS and pAD. Current smoking (inverse association) and depressive symptomatology were the only variables independently associated with PD. All reproductive disorders showed consistent associations with depression (measured either by depressive symptomatology or medication use) in both age-adjusted and multivariate analyses. Conclusion: A range of lifestyle factors, more often associated with chronic disease, were significantly associated with male reproductive health disorders. Education strategies directed to improving general health may also confer benefits to male reproductive health.Carol A. Holden, Robert I. McLachlan, Marian Pitts, Robert Cumming, Gary Wittert, Johnathon P. Ehsani, David M. de Kretser, David J. Handelsma