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Young stellar objects (YSOs) are protostars that exhibit bipolar outflows fed by accretion disks. Theories of the transition between disk and outflow often involve a complex magnetic field structure thought to be created by the disk coiling field lines at the jet base; however, due to limited resolution, these theories cannot be confirmed with observation and thus may benefit from laboratory astrophysics studies. We create a dynamically similar laboratory system by driving a$$\sim$$1 MA current pulse with a 200 ns rise through a$$\approx$$2 mm-tall Al cylindrical wire array mounted to a three-dimensional (3-D)-printed, stainless steel scaffolding. This system creates a plasma that converges on the centre axis and ejects cm-scale bipolar outflows. Depending on the chosen 3-D-printed load path, the system may be designed to push the ablated plasma flow radially inwards or off-axis to make rotation. In this paper, we present results from the simplest iteration of the load which generates radially converging streams that launch non-rotating jets. The temperature, velocity and density of the radial inflows and axial outflows are characterized using interferometry, gated optical and ultraviolet imaging, and Thomson scattering diagnostics. We show that experimental measurements of the Reynolds number and sonic Mach number in three different stages of the experiment scale favourably to the observed properties of YSO jets with$$Re\sim 10^5\unicode{x2013}10^9$$and$$M\sim 1\unicode{x2013}10$$, while our magnetic Reynolds number of$$Re_M\sim 1\unicode{x2013}15$$indicates that the magnetic field diffuses out of our plasma over multiple hydrodynamical time scales. We compare our results with 3-D numerical simulations in the PERSEUS extended magnetohydrodynamics code. 

Brain age (BA), distinct from chronological age (CA), can be estimated from MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individuals. BA, however, is a cross-sectional measure that summarizes cumulative neuroanatomic aging since birth. Thus, it conveys poorly recent or contemporaneous aging trends, which can be better quantified by the (temporal) pace P of brain aging. Many approaches to map P, however, rely on quantifying DNA methylation in whole-blood cells, which the blood–brain barrier separates from neural brain cells. We introduce a three-dimensional convolutional neural network (3D-CNN) to estimate P noninvasively from longitudinal MRI. Our longitudinal model (LM) is trained on MRIs from 2,055 CN adults, validated in 1,304 CN adults, and further applied to an independent cohort of 104 CN adults and 140 patients with Alzheimer’s disease (AD). In its test set, the LM computes P with a mean absolute error (MAE) of 0.16 y (7% mean error). This significantly outperforms the most accurate cross-sectional model, whose MAE of 1.85 y has 83% error. By synergizing the LM with an interpretable CNN saliency approach, we map anatomic variations in regional brain aging rates that differ according to sex, decade of life, and neurocognitive status. LM estimates of P are significantly associated with changes in cognitive functioning across domains. This underscores the LM’s ability to estimate P in a way that captures the relationship between neuroanatomic and neurocognitive aging. This research complements existing strategies for AD risk assessment that estimate individuals’ rates of adverse cognitive change with age. 

The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N  = 351) and Alzheimer’s disease (AD, N  = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk.