Taking Chances: The Effect of Growing Up on Welfare on the Risky Behavior of Young People

We analyze the effect of growing up on welfare on young people's involvement in a variety of social and health risks. Young people in welfare families are much more likely to take both social and health risks. Much of the apparent link between family welfare history and risk taking disappears, however, once we account for family structure and mothers' decisions regarding their own risk taking and investment in their children. Interestingly, we find no significant effect of socio-economic status per se. Overall, we find no evidence that growing up on welfare causes young people to engage in risky behavior.youths, welfare, risky behavior, socio-economic disadvantage

Optic radiation structure and anatomy in the normally developing brain determined using diffusion MRI and tractography

The optic radiation (OR) is a component of the visual system known to be myelin mature very early in life. Diffusion tensor imaging (DTI) and its unique ability to reconstruct the OR in vivo were used to study structural maturation through analysis of DTI metrics in a cohort of 90 children aged 5–18 years. As the OR is at risk of damage during epilepsy surgery, we measured its position relative to characteristic anatomical landmarks. Anatomical distances, DTI metrics and volume of the OR were investigated for age, gender and hemisphere effects. We observed changes in DTI metrics with age comparable to known trajectories in other white matter tracts. Left lateralization of DTI metrics was observed that showed a gender effect in lateralization. Sexual dimorphism of DTI metrics in the right hemisphere was also found. With respect to OR dimensions, volume was shown to be right lateralised and sexual dimorphism demonstrated for the extent of the left OR. The anatomical results presented for the OR have potentially important applications for neurosurgical planning

Fibre tract segmentation for intraoperative diffusion MRI in neurosurgical patients using tract-specific orientation atlas and tumour deformation modelling

Purpose:: Intraoperative diffusion MRI could provide a means of visualising brain fibre tracts near a neurosurgical target after preoperative images have been invalidated by brain shift. We propose an atlas-based intraoperative tract segmentation method, as the standard preoperative method, streamline tractography, is unsuitable for intraoperative implementation. Methods:: A tract-specific voxel-wise fibre orientation atlas is constructed from healthy training data. After registration with a target image, a radial tumour deformation model is applied to the orientation atlas to account for displacement caused by lesions. The final tract map is obtained from the inner product of the atlas and target image fibre orientation data derived from intraoperative diffusion MRI. Results:: The simple tumour model takes only seconds to effectively deform the atlas into alignment with the target image. With minimal processing time and operator effort, maps of surgically relevant tracts can be achieved that are visually and qualitatively comparable with results obtained from streamline tractography. Conclusion:: Preliminary results demonstrate feasibility of intraoperative streamline-free tract segmentation in challenging neurosurgical cases. Demonstrated results in a small number of representative sample subjects are realistic despite the simplicity of the tumour deformation model employed. Following this proof of concept, future studies will focus on achieving robustness in a wide range of tumour types and clinical scenarios, as well as quantitative validation of segmentations

Markov Chain Monte Carlo Random Effects Modeling in Magnetic Resonance Image Processing Using the BRugs Interface to WinBUGS

A common feature of many magnetic resonance image (MRI) data processing methods is the voxel-by-voxel (a voxel is a volume element) manner in which the processing is performed. In general, however, MRI data are expected to exhibit some level of spatial correlation, rendering an independent-voxels treatment inefficient in its use of the data. Bayesian random effect models are expected to be more efficient owing to their information-borrowing behaviour. To illustrate the Bayesian random effects approach, this paper outlines a Markov chain Monte Carlo (MCMC) analysis of a perfusion MRI dataset, implemented in R using the BRugs package. BRugs provides an interface to WinBUGS and its GeoBUGS add-on. WinBUGS is a widely used programme for performing MCMC analyses, with a focus on Bayesian random effect models. A simultaneous modeling of both voxels (restricted to a region of interest) and multiple subjects is demonstrated. Despite the low signal-to-noise ratio in the magnetic resonance signal intensity data, useful model signal intensity profiles are obtained. The merits of random effects modeling are discussed in comparison with the alternative approaches based on region-of-interest averaging and repeated independent voxels analysis. This paper focuses on perfusion MRI for the purpose of illustration, the main proposition being that random effects modeling is expected to be beneficial in many other MRI applications in which the signal-to-noise ratio is a limiting factor

An accurate calculation of the nucleon axial charge with lattice QCD

We report on a lattice QCD calculation of the nucleon axial charge, $g_A$, using M\"{o}bius Domain-Wall fermions solved on the dynamical $N_f=2+1+1$ HISQ ensembles after they are smeared using the gradient-flow algorithm. The calculation is performed with three pion masses, $m_\pi\sim\{310,220,130\}$ MeV. Three lattice spacings ($a\sim\{0.15,0.12,0.09\}$ fm) are used with the heaviest pion mass, while the coarsest two spacings are used on the middle pion mass and only the coarsest spacing is used with the near physical pion mass. On the $m_\pi\sim220$ MeV, $a\sim0.12$ fm point, a dedicated volume study is performed with $m_\pi L \sim \{3.22,4.29,5.36\}$. Using a new strategy motivated by the Feynman-Hellmann Theorem, we achieve a precise determination of $g_A$ with relatively low statistics, and demonstrable control over the excited state, continuum, infinite volume and chiral extrapolation systematic uncertainties, the latter of which remains the dominant uncertainty. Our final determination at 2.6\% total uncertainty is $g_A = 1.278(21)(26)$, with the first uncertainty including statistical and systematic uncertainties from fitting and the second including model selection systematics related to the chiral and continuum extrapolation. The largest reduction of the second uncertainty will come from a greater number of pion mass points as well as more precise lattice QCD results near the physical pion mass.Comment: 17 pages + 11 pages of references and appendices. 15 figures. Interested readers can download the Python analysis scripts and an hdf5 data file at https://github.com/callat-qcd/project_gA_v