Evidence of causal effect of major depression on alcohol dependence: findings from the psychiatric genomics consortium

BACKGROUND Despite established clinical associations among major depression (MD), alcohol dependence (AD), and alcohol consumption (AC), the nature of the causal relationship between them is not completely understood. We leveraged genome-wide data from the Psychiatric Genomics Consortium (PGC) and UK Biobank to test for the presence of shared genetic mechanisms and causal relationships among MD, AD, and AC. METHODS Linkage disequilibrium score regression and Mendelian randomization (MR) were performed using genome-wide data from the PGC (MD: 135 458 cases and 344 901 controls; AD: 10 206 cases and 28 480 controls) and UK Biobank (AC-frequency: 438 308 individuals; AC-quantity: 307 098 individuals). RESULTS Positive genetic correlation was observed between MD and AD (rgMD−AD = + 0.47, P = 6.6 × 10−10). AC-quantity showed positive genetic correlation with both AD (rgAD−AC quantity = + 0.75, P = 1.8 × 10−14) and MD (rgMD−AC quantity = + 0.14, P = 2.9 × 10−7), while there was negative correlation of AC-frequency with MD (rgMD−AC frequency = −0.17, P = 1.5 × 10−10) and a non-significant result with AD. MR analyses confirmed the presence of pleiotropy among these four traits. However, the MD-AD results reflect a mediated-pleiotropy mechanism (i.e. causal relationship) with an effect of MD on AD (beta = 0.28, P = 1.29 × 10−6). There was no evidence for reverse causation. CONCLUSION This study supports a causal role for genetic liability of MD on AD based on genetic datasets including thousands of individuals. Understanding mechanisms underlying MD-AD comorbidity addresses important public health concerns and has the potential to facilitate prevention and intervention efforts

Structure-Enhanced Attentive Learning for Spine Segmentation from Ultrasound Volume Projection Images

Automatic spine segmentation, based on ultrasound volume projection imaging (VPI), is of great value in clinical applications to diagnose scoliosis in teenagers. In this paper, we propose a novel framework to improve the segmentation accuracy on spine images via structure-enhanced attentive learning. Since the spine bones contain strong prior knowledge of their shapes and positions in ultrasound VPI images, we propose to encode this information into the semantic representations in an attentive manner. We first revisit the self-attention mechanism in representation learning, and then present a strategy to introduce the structural knowledge into the key representation in self-attention. By this means, the network explores both the contextual and structural information in the learned features, and consequently improves the segmentation accuracy. We conduct various experiments to demonstrate that our proposed method achieves promising performance on spine image segmentation, which shows great potential in clinical diagnosis

Predicting long-term urban growth in Beijing (China) with new factors and constraints of environmental change under integrated stochastic and fuzzy uncertainties

Numerous studies related to the simulation and prediction of urban growth to address land-use and land-cover (LULC) changes have been conducted in recent years, but very few have considered the impact of climate change, flooding impact, government relocation, corridor cities, and long-term rainfall variations simultaneously. To bridge the gap, this study predicts possible future LULC changes for 2030 and 2050 in Beijing (China), since Beijing is one of the fastest-growing megacities in the world. The proposed integrated modeling analysis covers four key scenarios to reflect the influences of different factors and constraints on LULC changes, in which cellular automata, Markov chain, and multi-criteria evaluation are fully coupled. While fuzzy membership function was used to address the uncertainty associated with the decision analysis, Markov chain, which is regarded as a stochastic process, was applied to predict future urban growth pathways. In addition, a statistical downscaling model driven by possible climate change scenarios was employed to address long-term rainfall variations in Beijing, China. This study differs from previous ones for Beijing in terms of not only the effects of climate change and flooding impact but also the newly-developed economic free trade zone in Xiong’an and the central government’s plan to relocate to the Tongzhou district. Findings indicate that there is no marked difference in LULC over the four key scenarios. Compared to the baseline LULC in 2010, the predicted results indicate that urban expansion is expected to increase more than 6 and 11% in 2030 and 2050, respectively