The review indicates greater prevalence of both major depression and CSDS compared to rates reported in the international literature, while the prevalence of dysthymia was found to be similar. The high prevalence of CSDS and its significant association with cardiovascular diseases reinforces the importance of evaluating subthreshold depressive symptoms in the elderly in the community.

Testing theories of hierarchical structure formation requires estimating the distribution of galaxy morphologies and its change with redshift. One aspect of this investigation involves identifying galaxies with disturbed morphologies (e.g., merging galaxies). This is often done by summarizing galaxy images using, e.g., the CAS and Gini-M 20 statistics of Conselice (2003) and Lotz et al. (2004), respectively, and associating particular statistic values with disturbance. We introduce three statistics that enhance detection of disturbed morphologies at high-redshift (z ∼ 2): the multi-mode (M ), intensity (I), and deviation (D) statistics. We show their effectiveness by training a machinelearning classifier, random forest, using 1,639 galaxies observed in the H-band by the Hubble Space Telescope WFC3, galaxies that had been previously classified by eye by the CANDELS collaboration (Grogin et al. 2011, Koekemoer et al. 2011. We find that the M ID statistics (and the A statistic of Conselice 2003) are the most useful for identifying disturbed morphologies.We also explore whether human annotators are useful for identifying disturbed morphologies. We demonstrate that they show limited ability to detect disturbance at high redshift, and that increasing their number beyond ≈10 does not provably yield better classification performance. We propose a simulation-based model-fitting algorithm that mitigates these issues by bypassing annotation.

Many scientific investigations of photometric galaxy surveys require redshift estimates, whose uncertainty properties are best encapsulated by photometric redshift (photo-z) posterior probability density functions (PDFs). A plethora of photo-z PDF estimation methodologies abound, producing discrepant results with no consensus on a preferred approach. We present the results of a comprehensive experiment comparing twelve photo-z algorithms applied to mock data produced forLarge Synoptic Survey Telescope The Rubin Observatory Legacy Survey of Space and Time (lsst) Dark Energy Science Collaboration (desc). By supplying perfect prior information, in the form of the complete template library and a representative training set as inputs to each code, we demonstrate the impact of the assumptions underlying each technique on the output photo-z PDFs. In the absence of a notion of true, unbiased photo-z PDFs, we evaluate and interpret multiple metrics of the ensemble properties of the derived photo-z PDFs as well as traditional reductions to photo-z point estimates. We report systematic biases and overall over/under-breadth of the photo-z PDFs of many popular codes, which may indicate avenues for improvement in the algorithms or implementations. Furthermore, we raise attention to the limitations of established metrics for assessing photo-z PDF accuracy; though we identify the conditional density estimate (CDE) loss as a promising metric of photo-z PDF performance in the case where true redshifts are available but true photo-z PDFs are not, we emphasize the need for science-specific performance metrics.

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