Accurate prediction of disease risk based on genetic factors is an important goal in human genetics research and precision medicine. Advanced prediction models will lead to more effective disease prevention and treatment strategies. Despite the identification of thousands of disease-associated genetic variants through genome-wide association studies (GWAS) in the past decade, accuracy of genetic risk prediction remains moderate for most diseases, which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes. In this work, we introduce PleioPred, a principled framework that leverages pleiotropy and functional annotations in genetic risk prediction for complex diseases. PleioPred uses GWAS summary statistics as its input, and jointly models multiple genetically correlated diseases and a variety of external information including linkage disequilibrium and diverse functional annotations to increase the accuracy of risk prediction. Through comprehensive simulations and real data analyses on Crohn’s disease, celiac disease and type-II diabetes, we demonstrate that our approach can substantially increase the accuracy of polygenic risk prediction and risk population stratification, i.e. PleioPred can significantly better separate type-II diabetes patients with early and late onset ages, illustrating its potential clinical application. Furthermore, we show that the increment in prediction accuracy is significantly correlated with the genetic correlation between the predicted and jointly modeled diseases.

The emergence of super-resolution (SR) fluorescence microscopy has rejuvenated the search for new cellular substructures. However, SR fluorescence microscopy achieves high contrast at the expense of a holistic view of the interacting partners and surrounding environment. Thus, we developed SR fluorescence-assisted diffraction computational tomography (SR-FACT), which combines label-free three-dimensional optical diffraction tomography (ODT) with two-dimensional fluorescence Hessian structured illumination microscopy. The ODT module is capable of resolving the mitochondria, lipid droplets, the nuclear membrane, chromosomes, the tubular endoplasmic reticulum, and lysosomes. Using dual-mode correlated live-cell imaging for a prolonged period of time, we observed novel subcellular structures named dark-vacuole bodies, the majority of which originate from densely populated perinuclear regions, and intensively interact with organelles such as the mitochondria and the nuclear membrane before ultimately collapsing into the plasma membrane. This work demonstrates the unique capabilities of SR-FACT, which suggests its wide applicability in cell biology in general.

Ellipse and hyperbola are two well-known curves in mathematics with numerous applications in various fields, but their properties and inherent differences in spin optics are less understood. Here, we investigate the peculiar optical spin properties of the two curves and establish a connection between their foci and the spin states of incident light. We show that the optical spin Hall effect is the intrinsic optical spin property of ellipse, where photons with different spin states can be exactly separated to each of its two foci. While a hyperbola exhibits optical spin-selective effect, where only photons with one particular spin state can be accumulated at its foci. These properties are then experimentally demonstrated in near field by arranging nanoslits in conic shape. Based on the spin properties of the curves, we design spin-based plasmonic devices with various functionalities. Our results reveal the intrinsic optical spin properties behind conic curves and provide a route for designing spin-based plasmonic device.

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