Studies of rodent models of Alzheimer’s disease (AD) and of human tissues suggest that the retinal changes that occur in AD, including the accumulation of amyloid beta (Aβ), may serve as surrogate markers of brain Aβ levels. As Aβ has a wavelength-dependent effect on light scatter, we investigate the potential for in vivo retinal hyperspectral imaging to serve as a biomarker of brain Aβ. Significant differences in the retinal reflectance spectra are found between individuals with high Aβ burden on brain PET imaging and mild cognitive impairment (n = 15), and age-matched PET-negative controls (n = 20). Retinal imaging scores are correlated with brain Aβ loads. The findings are validated in an independent cohort, using a second hyperspectral camera. A similar spectral difference is found between control and 5xFAD transgenic mice that accumulate Aβ in the brain and retina. These findings indicate that retinal hyperspectral imaging may predict brain Aβ load.
Machine learning (automated processes that learn by example in order to classify, predict, discover, or generate new data) and artificial intelligence (methods by which a computer makes decisions or discoveries that would usually require human intelligence) are now firmly established in astronomy. Every week, new applications of machine learning and artificial intelligence are added to a growing corpus of work. Random forests, support vector machines, and neural networks are now having a genuine impact for applications as diverse as discovering extrasolar planets, transient objects, quasars, and gravitationally lensed systems, forecasting solar activity, and distinguishing between signals and instrumental effects in gravitational wave astronomy. This review surveys contemporary, published literature on machine learning and artificial intelligence in astronomy and astrophysics. Applications span seven main categories of activity: classification, regression, clustering, forecasting, generation, discovery, and the development of new scientific insights. These categories form the basis of a hierarchy of maturity, as the use of machine learning and artificial intelligence emerges, progresses, or becomes established. This article is categorized under: Application Areas > Science and Technology Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining Technologies > Machine Learning
We present the results of weak gravitational lensing statistics in four different cosmological N‐body simulations. The data have been generated using an algorithm for the three‐dimensional shear, which makes use of a variable softening facility for the N‐body particle masses, and enables a physical interpretation for the large‐scale structure to be made. Working in three dimensions also allows the correct use of the appropriate angular diameter distances. Our results are presented on the basis of the filled‐beam approximation in view of the variable particle softening scheme in our algorithm. The importance of the smoothness of matter in the Universe for the weak lensing results is discussed in some detail. The low‐density cosmology with a cosmological constant appears to give the broadest distributions for all the statistics computed for sources at high redshifts. In particular, the range in magnification values for this cosmology has implications for the determination of the cosmological parameters from high‐redshift type Ia supernovae. The possibility of determining the density parameter from the non‐Gaussianity in the probability distribution for the convergence is discussed.
We use high-resolution H i data from the Westerbork H i Survey of Spiral and Irregular Galaxies (WHISP) to study the H i and angular momentum properties of a sample of 114 late-type galaxies. We explore the specific baryonic angular momentum–baryonic mass (jb–Mb) relation, and find that an unbroken power law of the form $j_\mathrm{ b} \propto M_\mathrm{ b}^{0.55 \pm 0.02}$ fits the data well, with an intrinsic scatter of ∼0.13 ± 0.01 dex. We revisit the relation between the atomic gas fraction, fatm, and the integrated atomic stability parameter q (the fatm–q relation), originally introduced by Obreschkow et al., and probe this parameter space by populating it with galaxies from different environments, in order to study the influence of the environment on their jb, fatm, and q values. We find evidence that galaxies with close neighbours show a larger intrinsic scatter about the fatm–q relation compared to galaxies without close neighbours. We also find enhanced star formation rate among the deviating galaxies with close neighbours. In addition, we use the bulge-to-total (B/T) ratio as a morphology proxy, and find a general trend of decreasing B/T values with increasing disc stability and H i fraction in the fatm–q plane, indicating a fundamental link between mass, specific angular momentum, gas fraction, and morphology of galaxies.
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