1,139 research outputs found
Autoplot: A browser for scientific data on the web
Autoplot is software developed for the Virtual Observatories in Heliophysics
to provide intelligent and automated plotting capabilities for many typical
data products that are stored in a variety of file formats or databases.
Autoplot has proven to be a flexible tool for exploring, accessing, and viewing
data resources as typically found on the web, usually in the form of a
directory containing data files with multiple parameters contained in each
file. Data from a data source is abstracted into a common internal data model
called QDataSet. Autoplot is built from individually useful components, and can
be extended and reused to create specialized data handling and analysis
applications and is being used in a variety of science visualization and
analysis applications. Although originally developed for viewing
heliophysics-related time series and spectrograms, its flexible and generic
data representation model makes it potentially useful for the Earth sciences.Comment: 16 page
The Ethical Review of Health Care Quality Improvement Initiatives: Findings From the Field
Based on surveys, examines the review mechanisms of quality improvement initiatives, including frequency; type, such as use of independent review boards; and consideration for ethical issues such as minimal risk and patient privacy and confidentiality
The impact, cost and cost-effectiveness of oral pre-exposure prophylaxis in sub-Saharan Africa: a scoping review of modelling contributions and way forward
Introduction: Oral pre-exposure prophylaxis (PrEP) is a new form of HIV prevention being considered for inclusion in national prevention portfolios. Many mathematical modelling studies have been undertaken that speak to the impact, cost and cost-effectiveness of PrEP programmes. We assess the available evidence from mathematical modelling studies to inform programme planning and policy decision making for PrEP and further research directions. Methods: We conducted a scoping review of the published modelling literature. Articles published in English which modelled oral PrEP in sub-Saharan Africa, or non-specific settings with relevance to generalised HIV epidemic settings, were included. Data were extracted for the strategies of PrEP use modelled, and the impact, cost and cost-effectiveness of PrEP for each strategy. We define an algorithm to assess the quality and relevance of studies included, summarise the available evidence and identify the current gaps in modelling. Recommendations are generated for future modelling applications and data collection. Results and discussion: We reviewed 1,924 abstracts and included 44 studies spanning 2007 to 2017. Modelling has reported that PrEP can be a cost-effective addition to HIV prevention portfolios for some use cases, but also that it would not be cost-effective to fund PrEP before other prevention intervention are expanded. However, our assessment of the quality of the modelling indicates cost-effectiveness analyses failed to comply with standards of reporting for economic evaluations and the assessment of relevance highlighted that both key parameters and scenarios are now outdated. Current evidence gaps include modelling to inform service development using updated programmatic information and ex post modelling to evaluate and inform efficient deployment of resources in support of PrEP, especially among key populations, using direct evidence of cost, adherence and uptake patterns. Conclusions: Updated modelling which more appropriately captures PrEP programme delivery, uses current intervention scenarios, and is parameterised with data from demonstration and implementation projects is needed in support of more conclusive findings and actionable recommendations for programmes and policy. Future analyses should address these issues, aligning with countries to support the needs of programme planners and decision makers for models to more directly inform programme planning and policy
{beta}3GnT2 Maintains Adenylyl Cyclase-3 Signaling and Axon Guidance Molecule Expression in the Olfactory Epithelium
In the olfactory epithelium (OE), odorant receptor stimulation generates cAMP signals that function in both odor detection and the regulation of axon guidance molecule expression. The enzyme that synthesizes cAMP, adenylyl cyclase 3 (AC3), is coexpressed in olfactory sensory neurons (OSNs) with poly-N-acetyllactosamine (PLN) oligosaccharides determined by the glycosyltransferase beta3GnT2. The loss of either enzyme results in similar defects in olfactory bulb (OB) innervation and OSN survival, suggesting that glycosylation may be important for AC3 function. We show here that AC3 is extensively modified with N-linked PLN, which is essential for AC3 activity and localization. On Western blots, AC3 from the wild-type OE migrates diffusely as a heavily glycosylated 200 kDa band that interacts with the PLN-binding lectin LEA. AC3 from the beta3GnT2(-/-) OE loses these PLN modifications, migrating instead as a 140 kDa glycoprotein. Furthermore, basal and forskolin-stimulated cAMP production is reduced 80-90% in the beta3GnT2(-/-) OE. Although AC3 traffics normally to null OSN cilia, it is absent from axon projections that aberrantly target the OB. The cAMP-dependent guidance receptor neuropilin-1 is also lost from beta3GnT2(-/-) OSNs and axons, while semaphorin-3A ligand expression is upregulated. In addition, kirrel2, a mosaically expressed adhesion molecule that functions in axon sorting, is absent from beta3GnT2(-/-) OB projections. These results demonstrate that PLN glycans are essential in OSNs for proper AC3 localization and function. We propose that the loss of cAMP-dependent guidance cues is also a critical factor in the severe axon guidance defects observed in beta3GnT2(-/-) mice
A categorical foundation for Bayesian probability
Given two measurable spaces and with countably generated
-algebras, a perfect prior probability measure on and a
sampling distribution , there is a corresponding inference
map which is unique up to a set of measure zero. Thus,
given a data measurement , a posterior probability
can be computed. This procedure is iterative: with
each updated probability , we obtain a new joint distribution which in
turn yields a new inference map and the process repeats with each
additional measurement. The main result uses an existence theorem for regular
conditional probabilities by Faden, which holds in more generality than the
setting of Polish spaces. This less stringent setting then allows for
non-trivial decision rules (Eilenberg--Moore algebras) on finite (as well as
non finite) spaces, and also provides for a common framework for decision
theory and Bayesian probability.Comment: 15 pages; revised setting to more clearly explain how to incorporate
perfect measures and the Giry monad; to appear in Applied Categorical
Structure
The ebb and flow of identity formation and competence development in sub-specialty residents: study of a continuity training setting
Olfactory discrimination largely persists in mice with defects in odorant receptor expression and axon guidance
<p>Abstract</p> <p>Background</p> <p>The defining feature of the main olfactory system in mice is that each olfactory sensory neuron expresses only one of more than a thousand different odorant receptor genes. Axons expressing the same odorant receptor converge onto a small number of targets in the olfactory bulb such that each glomerulus is made up of axon terminals expressing just one odorant receptor. It is thought that this precision in axon targeting is required to maintain highly refined odor discrimination. We previously showed that β3GnT2<sup>−/−</sup> mice have severe developmental and axon guidance defects. The phenotype of these mice is similar to adenylyl cyclase 3 (AC3) knockout mice largely due to the significant down-regulation of AC3 activity in β3GnT2<sup>−/−</sup> neurons.</p> <p>Results</p> <p>Microarray analysis reveals that nearly one quarter of all odorant receptor genes are down regulated in β3GnT2<sup>−/−</sup> mice compared to controls. Analysis of OR expression by quantitative PCR and <it>in situ</it> hybridization demonstrates that the number of neurons expressing some odorant receptors, such as mOR256-17, is increased by nearly 60% whereas for others such as mOR28 the number of neurons is decreased by more than 75% in β3GnT2<sup>−/−</sup> olfactory epithelia. Analysis of axon trajectories confirms that many axons track to inappropriate targets in β3GnT2<sup>−/−</sup> mice, and some glomeruli are populated by axons expressing more than one odorant receptor. Results show that mutant mice perform nearly as well as control mice in an odor discrimination task. In addition, <it>in situ</it> hybridization studies indicate that the expression of several activity dependent genes is unaffected in β3GnT2<sup>−/−</sup> olfactory neurons.</p> <p>Conclusions</p> <p>Results presented here show that many odorant receptors are under-expressed in β3GnT2<sup>−/−</sup> mice and further demonstrate that additional axon subsets grow into inappropriate targets or minimally innervate glomeruli in the olfactory bulb. Odor evoked gene expression is unchanged and β3GnT2<sup>−/−</sup> mice exhibit a relatively small deficit in their ability to discriminate divergent odors. Results suggest that despite the fact that β3GnT2<sup>−/−</sup> mice have decreased AC3 activity, decreased expression of many ORs, and display many axon growth and guidance errors, odor-evoked activity in cilia of mutant olfactory neurons remains largely intact.</p
Perivascular Expression and Potent Vasoconstrictor Effect of Dynorphin A in Cerebral Arteries
BACKGROUND: Numerous literary data indicate that dynorphin A (DYN-A) has a significant impact on cerebral circulation, especially under pathophysiological conditions, but its potential direct influence on the tone of cerebral vessels is obscure. The aim of the present study was threefold: 1) to clarify if DYN-A is present in cerebral vessels, 2) to determine if it exerts any direct effect on cerebrovascular tone, and if so, 3) to analyze the role of κ-opiate receptors in mediating the effect. METHODOLOGY/PRINCIPAL FINDINGS: Immunohistochemical analysis revealed the expression of DYN-A in perivascular nerves of rat pial arteries as well as in both rat and human intraparenchymal vessels of the cerebral cortex. In isolated rat basilar and middle cerebral arteries (BAs and MCAs) DYN-A (1-13) and DYN-A (1-17) but not DYN-A (1-8) or dynorphin B (DYN-B) induced strong vasoconstriction in micromolar concentrations. The maximal effects, compared to a reference contraction induced by 124 mM K(+), were 115±6% and 104±10% in BAs and 113±3% and 125±9% in MCAs for 10 µM of DYN-A (1-13) and DYN-A (1-17), respectively. The vasoconstrictor effects of DYN-A (1-13) could be inhibited but not abolished by both the κ-opiate receptor antagonist nor-Binaltorphimine dihydrochloride (NORBI) and blockade of G(i/o)-protein mediated signaling by pertussis toxin. Finally, des-Tyr(1) DYN-A (2-13), which reportedly fails to activate κ-opiate receptors, induced vasoconstriction of 45±11% in BAs and 50±5% in MCAs at 10 µM, which effects were resistant to NORBI. CONCLUSION/SIGNIFICANCE: DYN-A is present in rat and human cerebral perivascular nerves and induces sustained contraction of rat cerebral arteries. This vasoconstrictor effect is only partly mediated by κ-opiate receptors and heterotrimeric G(i/o)-proteins. To our knowledge our present findings are the first to indicate that DYN-A has a direct cerebral vasoconstrictor effect and that a dynorphin-induced vascular action may be, at least in part, independent of κ-opiate receptors
Nasopharyngeal flora in children with acute otitis media before and after implementation of 7 valent pneumococcal conjugate vaccine in France
Exact Bayesian curve fitting and signal segmentation.
We consider regression models where the underlying functional relationship between the response and the explanatory variable is modeled as independent linear regressions on disjoint segments. We present an algorithm for perfect simulation from the posterior distribution of such a model, even allowing for an unknown number of segments and an unknown model order for the linear regressions within each segment. The algorithm is simple, can scale well to large data sets, and avoids the problem of diagnosing convergence that is present with Monte Carlo Markov Chain (MCMC) approaches to this problem. We demonstrate our algorithm on standard denoising problems, on a piecewise constant AR model, and on a speech segmentation problem
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