Sodium (Na) ultra-short echo time imaging in the human brain using a 3D-Cones trajectory

Object: Sodium magnetic resonance imaging (Na-MRI) of the brain has shown changes in Na signal as a hallmark of various neurological diseases such as stroke, Alzheimer's disease, Multiple Sclerosis and Huntington's disease. To improve scan times and image quality, we have implemented the 3D-Cones (CN) sequence for in vivo Na brain MRI. Materials and methods: Using signal-to-noise (SNR) as a measurement of sequence performance, CN is compared against more established 3D-radial k-space sampling schemes featuring cylindrical stack-of-stars (SOS) and 3D-spokes kooshball (KB) trajectories, on five healthy volunteers in a clinical setting. Resolution was evaluated by simulating the point-spread-functions (PSFs) and experimental measures on a phantom. Results: All sequences were shown to have a similar SNR arbitrary units (AU) of 6-6.5 in brain white matter, 7-9 in gray matter and 17-18 AU in cerebrospinal fluid. SNR between white and gray matter were significantly different for KB and CN (p = 0.046 and <0.001 respectively), but not for SOS (p = 0.1). Group mean standard deviations were significantly smaller for CN (p = 0.016). Theoretical full-width at half-maximum linewidth of the PSF for CN is broadened by only 0.1, compared to 0.3 and 0.8 pixels for SOS and KB respectively. Actual image resolution is estimated as 8, 9 and 6.3 mm for SOS, KB and CN respectively. Conclusion: The CN sequence provides stronger tissue contrast than both SOS and KB, with more reproducible SNR measurements compared to KB. For CN, a higher true resolution in the same amount of time with no significant trade-off in SNR is achieved. CN is therefore more suitable for Na-MRI in the brain. © 2013 The Author(s)

Sodium ((23)Na) ultra-short echo time imaging in the human brain using a 3D-Cones trajectory

Object: Sodium magnetic resonance imaging ((23)Na-MRI) of the brain has shown changes in (23)Na signal as a hallmark of various neurological diseases such as stroke, Alzheimer's disease, Multiple Sclerosis and Huntington's disease. To improve scan times and image quality, we have implemented the 3D-Cones (CN) sequence for in vivo (23)Na brain MRI. Materials and Methods: Using signal-to-noise (SNR) as a measurement of sequence performance, CN is compared against more established 3D-radial k-space sampling schemes featuring cylindrical stack-of-stars (SOS) and 3D-spokes kooshball (KB) trajectories, on five healthy volunteers in a clinical setting. Resolution was evaluated by simulating the point-spread-functions (PSFs) and experimental measures on a phantom. Results: All sequences were shown to have a similar SNR arbitrary units (AU) of 6–6.5 in brain white matter, 7–9 in gray matter and 17–18 AU in cerebrospinal fluid. SNR between white and gray matter were significantly different for KB and CN (p = 0.046 and\0.001 respectively), but not for SOS (p = 0.1). Group mean standard deviations were significantly smaller for CN (p = 0.016). Theoretical full-width at half-maximum linewidth of the PSF for CN is broadened by only 0.1, compared to 0.3 and 0.8 pixels for SOS and KB respectively. Actual image resolution is estimated as 8, 9 and 6.3 mm for SOS, KB and CN respectively. Conclusion: The CN sequence provides stronger tissue contrast than both SOS and KB, with more reproducible SNR measurements compared to KB. For CN, a higher true resolution in the same amount of time with no significant trade-off in SNR is achieved. CN is therefore more suitable for 23Na-MRI in the brain

Spatial navigation under threat: aversive apprehensions improve route retracing in higher versus lower trait anxious individuals

Spatial navigation is a basic function for survival, and the ability to retrace a route has direct relevance for avoiding dangerous places. This study investigates the effects of aversive apprehensions on spatial navigation in a virtual urban environment. Healthy participants with varying degrees of trait anxiety performed a route-repetition and a route-retracing task under threatening and safe context conditions. Results reveal an interaction between the effect of threatening/safe environments and trait anxiety: while threat impairs route-retracing in lower-anxious individuals, this navigational skill is boosted in higher-anxious individuals. According to attentional control theory, this finding can be explained by an attentional shift toward information relevant for intuitive coping strategies (i.e., running away), which should be more pronounced in higher-anxious individuals. On a broader scale, our results demonstrate an often-neglected advantage of trait anxiety, namely that it promotes the processing of environmental information relevant for coping strategies and thus prepares the organism for adequate flight responses

Robust Bayes-Like Estimation: Rho-Bayes estimation

We consider the problem of estimating the joint distribution $P$ of $n$ independent random variables within the Bayes paradigm from a non-asymptotic point of view. Assuming that $P$ admits some density $s$ with respect to a given reference measure, we consider a density model $\overline S$ for $s$ that we endow with a prior distribution $\pi$ (with support $\overline S$) and we build a robust alternative to the classical Bayes posterior distribution which possesses similar concentration properties around $s$ whenever it belongs to the model $\overline S$. Furthermore, in density estimation, the Hellinger distance between the classical and the robust posterior distributions tends to 0, as the number of observations tends to infinity, under suitable assumptions on the model and the prior, provided that the model $\overline S$ contains the true density $s$. However, unlike what happens with the classical Bayes posterior distribution, we show that the concentration properties of this new posterior distribution are still preserved in the case of a misspecification of the model, that is when $s$ does not belong to $\overline S$ but is close enough to it with respect to the Hellinger distance.Comment: 68 page

Homogeneous bubble nucleation limit of mercury under the normal working conditions of the planned European Spallation Source

In spallation neutron sources, liquid mercury is the subject of big thermal and pressure shocks, upon adsorbing the proton beam. These changes can cause unstable bubbles in the liquid, which can damage the structural material. While there are methods to deal with the pressure shock, the local temperature shock cannot be avoided. In our paper we calculated the work of the critical cluster formation (i.e. for mercury micro-bubbles) together with the rate of their formation (nucleation rate). It is shown that the homogeneous nucleation rates are very low even after adsorbing several proton pulses, therefore the probability of temperature induced homogeneous bubble nucleation is negligible.Comment: 22 Pages, 11 figures, one of them is colour, we plan to publish it in Eur. Phys. J.

Lightest sterile neutrino abundance within the nuMSM

We determine the abundance of the lightest (dark matter) sterile neutrinos created in the Early Universe due to active-sterile neutrino transitions from the thermal plasma. Our starting point is the field-theoretic formula for the sterile neutrino production rate, derived in our previous work [JHEP 06(2006)053], which allows to systematically incorporate all relevant effects, and also to analyse various hadronic uncertainties. Our numerical results differ moderately from previous computations in the literature, and lead to an absolute upper bound on the mixing angles of the dark matter sterile neutrino. Comparing this bound with existing astrophysical X-ray constraints, we find that the Dodelson-Widrow scenario, which proposes sterile neutrinos generated by active-sterile neutrino transitions to be the sole source of dark matter, is only possible for sterile neutrino masses lighter than 3.5 keV (6 keV if all hadronic uncertainties are pushed in one direction and the most stringent X-ray bounds are relaxed by a factor of two). This upper bound may conflict with a lower bound from structure formation, but a definitive conclusion necessitates numerical simulations with the non-equilibrium momentum distribution function that we derive. If other production mechanisms are also operative, no upper bound on the sterile neutrino mass can be established.Comment: 34 pages. v2: clarifications and a reference added; published version. v3: erratum appende

Continuous melting of compact polymers

The competition between chain entropy and bending rigidity in compact polymers can be addressed within a lattice model introduced by P.J. Flory in 1956. It exhibits a transition between an entropy dominated disordered phase and an energetically favored crystalline phase. The nature of this order-disorder transition has been debated ever since the introduction of the model. Here we present exact results for the Flory model in two dimensions relevant for polymers on surfaces, such as DNA adsorbed on a lipid bilayer. We predict a continuous melting transition, and compute exact values of critical exponents at the transition point.Comment: 5 pages, 1 figur

Global atmospheric budget of acetaldehyde: 3-D model analysis and constraints from in-situ and satellite observations

We construct a global atmospheric budget for acetaldehyde using a 3-D model of atmospheric chemistry (GEOS-Chem), and use an ensemble of observations to evaluate present understanding of its sources and sinks. Hydrocarbon oxidation provides the largest acetaldehyde source in the model (128 Tg a&lt;sup&gt;&amp;minus;1&lt;/sup&gt;, a factor of 4 greater than the previous estimate), with alkanes, alkenes, and ethanol the main precursors. There is also a minor source from isoprene oxidation. We use an updated chemical mechanism for GEOS-Chem, and photochemical acetaldehyde yields are consistent with the Master Chemical Mechanism. We present a new approach to quantifying the acetaldehyde air-sea flux based on the global distribution of light absorption due to colored dissolved organic matter (CDOM) derived from satellite ocean color observations. The resulting net ocean emission is 57 Tg a&lt;sup&gt;&amp;minus;1&lt;/sup&gt;, the second largest global source of acetaldehyde. A key uncertainty is the acetaldehyde turnover time in the ocean mixed layer, with quantitative model evaluation over the ocean complicated by known measurement artifacts in clean air. Simulated concentrations in surface air over the ocean generally agree well with aircraft measurements, though the model tends to overestimate the vertical gradient. PAN:NO&lt;sub&gt;x&lt;/sub&gt; ratios are well-simulated in the marine boundary layer, providing some support for the modeled ocean source. We introduce the Model of Emissions of Gases and Aerosols from Nature (MEGANv2.1) for acetaldehyde and ethanol and use it to quantify their net flux from living terrestrial plants. Including emissions from decaying plants the total direct acetaldehyde source from the land biosphere is 23 Tg a&lt;sup&gt;&amp;minus;1&lt;/sup&gt;. Other terrestrial acetaldehyde sources include biomass burning (3 Tg a&lt;sup&gt;&amp;minus;1&lt;/sup&gt;) and anthropogenic emissions (2 Tg a&lt;sup&gt;&amp;minus;1&lt;/sup&gt;). Simulated concentrations in the continental boundary layer are generally unbiased and capture the spatial gradients seen in observations over North America, Europe, and tropical South America. However, the model underestimates acetaldehyde levels in urban outflow, suggesting a missing source in polluted air. Ubiquitous high measured concentrations in the free troposphere are not captured by the model, and based on present understanding are not consistent with concurrent measurements of PAN and NO&lt;sub&gt;x&lt;/sub&gt;: we find no compelling evidence for a widespread missing acetaldehyde source in the free troposphere. We estimate the current US source of ethanol and acetaldehyde (primary + secondary) at 1.3 Tg a&lt;sup&gt;&amp;minus;1&lt;/sup&gt; and 7.8 Tg a&lt;sup&gt;&amp;minus;1&lt;/sup&gt;, approximately 60{%} and 480% of the corresponding increases expected for a national transition from gasoline to ethanol fuel