On The Age Estimation of LBDS 53W091

The recent spectral analysis of LBDS 53W091 by Spinrad and his collaborators has suggested that this red galaxy at z=1.55 is at least 3.5 Gyr old. This imposes an important constraint on cosmology, suggesting that this galaxy formed at z > 6.5, assuming recent estimates of cosmological parameters. We have performed chi^2 tests to the continuum of this galaxy using its UV spectrum and photometric data (RJHK). We have used the updated Yi models that are based on the Yale tracks. We find it extremely difficult to reproduce such large age estimates, under the assumption of the most probable input parameters. Using the same configuration as in Spinrad et al. (solar abundance models), our analysis suggests an age of approximately 1.4 -- 1.8 Gyr. The discrepancy between Spinrad et al.'s age estimate (based on the 1997 Jimenez models) and ours originates from the large difference in the model integrated spectrum: the Jimenez models are much bluer than the Yi models and the Bruzual \& Charlot (BC) models. Preliminary tests favor the Yi and BC models. The updated age estimate of LBDS 53W091 would suggest that this galaxy formed approximately at z=2-3.Comment: LaTeX, 18 eps files Accepted for publication in ApJ (Feb 10, 2000, vol 530), uses emulateapj.st

Postnatal gestational age estimation of newborns using Small Sample Deep Learning

© 2018 A baby's gestational age determines whether or not they are premature, which helps clinicians decide on suitable post-natal treatment. The most accurate dating methods use Ultrasound Scan (USS) machines, but these are expensive, require trained personnel and cannot always be deployed to remote areas. In the absence of USS, the Ballard Score, a postnatal clinical examination, can be used. However, this method is highly subjective and results vary widely depending on the experience of the examiner. Our main contribution is a novel system for automatic postnatal gestational age estimation using small sets of images of a newborn's face, foot and ear. Our two-stage architecture makes the most out of Convolutional Neural Networks trained on small sets of images to predict broad classes of gestational age, and then fuses the outputs of these discrete classes with a baby's weight to make fine-grained predictions of gestational age using Support Vector Regression. On a purpose-collected dataset of 130 babies, experiments show that our approach surpasses current automatic state-of-the-art postnatal methods and attains an expected error of 6 days. It is three times more accurate than the Ballard method. Making use of images improves predictions by 33% compared to using weight only. This indicates that even with a very small set of data, our method is a viable candidate for postnatal gestational age estimation in areas were USS is not available

Age estimation [editorial].

yesAssessing and interpreting dental and skeletal age-related changes in both the living and the dead is of interest to a wide range of disciplines (e.g. see Bittles and Collins 1986) including human biology, paediatrics, public health, palaeodemography, archaeology, palaeontology, human evolution, forensic anthropology and legal medicine. ... This special issue of Annals of Human Biology arises from the 55th annual symposium of the Society for the Study of Human Biology in association with the British Association for Biological Anthropological and Osteoarchaeology held in Oxford, UK, from 9–11 December 2014. Only a selection of the presentations are included here which encompass some of the major recent advances in age estimation from the dentition and skeleton