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In this paper, we presented an interactive method named as Self-Adaptive Blur (SAB). By altering the sharpness of display according to the context, the SAB method was designed to prevent common discomforts of computer users by helping them maintain healthy posture. Based on previous pathophysiological studies, the causation of discomfort, mainly of neck and shoulder pain, was reviewed to select effective factors. A functioning prototype was developed using Microsoft Kinect and a desktop client program. The efficacy of our persuasive practice was examined by a series of experiments with the prototype. Objective records demonstrated the effectiveness of the SAB method, while subjective questionnaires and interviews revealed other instructive findings for further improvements.

Although good application effect of locating faulty section in complex distribution network can be attained by applying ant colony algorithm, however there are also disadvantages such as too long search time and computation time. For this reason, traditional ant colony algorithm should be improved. Firstly, a switching function that can dynamically adapt to topological structure of distribution network is constructed; then a method to set the initial values of ant colony's pheromone and the disturbance rule of the solution are led in; finally the method to apply the improved ant colony in fault section location of distribution network with multi power supplies is determined. The advantages of the improved ant colony algorithm in calculation speed and error tolerance are verified by results of calculation example.

Great efforts are being made to develop advanced polygenic risk scores (PRS) to improve the prediction of complex traits and diseases. However, most existing PRS are primarily trained on European ancestry populations, limiting their transferability to non-European populations. In this article, we propose a novel method for generating multi-ancestry Polygenic Risk scOres based on enSemble of PEnalized Regression models (PROSPER). PROSPER integrates genome-wide association studies (GWAS) summary statistics from diverse populations to develop ancestry-specific PRS with improved predictive power for minority populations. The method uses a combination of ℒ 1 (lasso) and ℒ 2 (ridge) penalty functions, a parsimonious specification of the penalty parameters across populations, and an ensemble step to combine PRS generated across different penalty parameters. We evaluate the performance of PROSPER and other existing methods on large-scale simulated and real datasets, including those from 23andMe Inc., the Global Lipids Genetics Consortium, and All of Us. Results show that PROSPER can substantially improve multi-ancestry polygenic prediction compared to alternative methods across a wide variety of genetic architectures. In real data analyses, for example, PROSPER increased out-of-sample prediction R 2 for continuous traits by an average of 70% compared to a state-of-the-art Bayesian method (PRS-CSx) in the African ancestry population. Further, PROSPER is computationally highly scalable for the analysis of large SNP contents and many diverse populations., Competing Interests: Competing Interests Statement J.Zhan, J.O., Y.J. and B.L.K. are employed by and hold stock or stock options in 23andMe, Inc. The remaining authors declare no competing interests.

Polygenic risk scores (PRS) are now showing promising predictive performance on a wide variety of complex traits and diseases, but there exists a substantial performance gap across different populations. We propose MUSSEL, a method for ancestry-specific polygenic prediction that borrows information in the summary statistics from genome-wide association studies (GWAS) across multiple ancestry groups. MUSSEL conducts Bayesian hierarchical modeling under a MUltivariate Spike-and-Slab model for effect-size distribution and incorporates an Ensemble Learning step using super learner to combine information across different tuning parameter settings and ancestry groups. In our simulation studies and data analyses of 16 traits across four distinct studies, totaling 5.7 million participants with a substantial ancestral diversity, MUSSEL shows promising performance compared to alternatives. The method, for example, has an average gain in prediction R 2 across 11 continuous traits of 40.2% and 49.3% compared to PRS-CSx and CT-SLEB, respectively, in the African Ancestry population. The best-performing method, however, varies by GWAS sample size, target ancestry, underlying trait architecture, and the choice of reference samples for LD estimation, and thus ultimately, a combination of methods may be needed to generate the most robust PRS across diverse populations., Competing Interests: Declaration of interests J.Zhan, Y.J., J.O., and B.L.K. are employed by and hold stock or stock options in 23andMe, Inc.