Risk factors for incidence and persistence of disability in chronic major depression and alcohol use disorders: longitudinal analyses of a population-based study

BackgroundMajor depression and alcohol use disorders are risk factors for incidence of disability. However, it is still unclear whether a chronic course of these health conditions is also prospectively associated with incidence of disability. The aim of the present study was, first, to confirm whether chronic major depression (MD) and alcohol use disorders (AUD) are, respectively, risk factors for persistence and incidence of disability in the general population; and then to analyze the role of help-seeking behavior in the course of disability among respondents with chronic MD and chronic AUD. MethodData from two assessments in the National Epidemiologic Survey on Alcohol and Related Conditions were analyzed. Disability was measured by eight domains of the Short Form 12 Health Survey version 2 (SF-12). Generalized estimating equations and logistic regression models were run to estimate risk factors for persistence and incidence of disability, respectively. ResultsAnalyses conducted on data from the US general population showed that chronic MD was the strongest risk factor for incidence and persistence of disability in the social functioning, emotional role and mental health domains. Chronic AUD were risk factors for incidence and persistence of disability in the vitality, social functioning, and emotional role domains. Within the group of chronic MD, physical comorbidity and help-seeking were associated with persistent disability in most of the SF-12 domains. Help-seeking behavior was also associated with incidence of problems in the mental health domain for the depression group. Regarding the AUD group, comorbidity with physical health problems was a strong risk factor for persistence of disability in all SF-12 domains. Help-seeking behavior was not related to either persistence or incidence of disability in the chronic alcohol group. ConclusionsChronic MD and chronic AUD are independent risk factors for persistence and incidence of disability in the US general population. People with chronic MD seek help for their problems when they experience persistent disability, whereas people with chronic AUD might not seek any help even if they are suffering from persistent disability.<br/

Reaching the boundary between stellar kinematic groups and very wide binaries. III. Sixteen new stars and eight new wide systems in the beta Pictoris moving group

Aims. We look for common proper motion companions to stars of the nearby young beta Pictoris moving group. Methods. First, we compiled a list of 185 beta Pictoris members and candidate members from 35 representative works. Next, we used the Aladin and STILTS virtual observatory tools, and the PPMXL proper motion and Washington Double Star catalogues to look for companion candidates. The resulting potential companions were subjects of a dedicated astro-photometric follow-up using public data from all-sky surveys. After discarding 67 sources by proper motion and 31 by colour-magnitude diagrams, we obtained a final list of 36 common proper motion systems. The binding energy of two of them is perhaps too small to be considered physically bound. Results. Of the 36 pairs and multiple systems, eight are new, 16 have only one stellar component previously classified as a beta Pictoris member, and three have secondaries at or below the hydrogen-burning limit. Sixteen stars are reported here for the first time as moving group members. The unexpected large number of high-order multiple systems, 12 triples and two quadruples among 36 systems, may suggest a biased list of members towards close binaries or an increment of the high-order-multiple fraction for very wide systems.Comment: A&A in pres

Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network

Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly bicubic interpolation, before reconstruction. This means that the super-resolution (SR) operation is performed in HR space. We demonstrate that this is sub-optimal and adds computational complexity. In this paper, we present the first convolutional neural network (CNN) capable of real-time SR of 1080p videos on a single K2 GPU. To achieve this, we propose a novel CNN architecture where the feature maps are extracted in the LR space. In addition, we introduce an efficient sub-pixel convolution layer which learns an array of upscaling filters to upscale the final LR feature maps into the HR output. By doing so, we effectively replace the handcrafted bicubic filter in the SR pipeline with more complex upscaling filters specifically trained for each feature map, whilst also reducing the computational complexity of the overall SR operation. We evaluate the proposed approach using images and videos from publicly available datasets and show that it performs significantly better (+0.15dB on Images and +0.39dB on Videos) and is an order of magnitude faster than previous CNN-based methods