Implications of pleiotropy: challenges and opportunities for mining Big Data in biomedicine

Pleiotropy arises when a locus influences multiple traits. Rich GWAS findings of various traits in the past decade reveal many examples of this phenomenon, suggesting the wide existence of pleiotropic effects. What underlies this phenomenon is the biological connection among seemingly unrelated traits/diseases. Characterizing the molecular mechanisms of pleiotropy not only helps to explain the relationship between diseases, but may also contribute to novel insights concerning the pathological mechanism of each specific disease, leading to better disease prevention, diagnosis and treatment. However, most pleiotropic effects remain elusive because their functional roles have not been systematically examined. A systematic investigation requires availability of qualified measurements at multilayered biological processes (e.g., transcription and translation).The rise of Big Data in biomedicine, such as high-quality multi-omics data, biomedical imaging data and electronic medical records of patients, offers us an unprecedented opportunity to investigate pleiotropy. There will be a great need of computationally efficient and statistically rigorous methods for integrative analysis of these Big Data in biomedicine. In this review, we outline many opportunities and challenges in methodology developments for systematic analysis of pleiotropy, and highlight its implications on disease prevention, diagnosis and treatment

SHA-SCP: A UI Element Spatial Hierarchy Aware Smartphone User Click Behavior Prediction Method

Predicting user click behavior and making relevant recommendations based on the user's historical click behavior are critical to simplifying operations and improving user experience. Modeling UI elements is essential to user click behavior prediction, while the complexity and variety of the UI make it difficult to adequately capture the information of different scales. In addition, the lack of relevant datasets also presents difficulties for such studies. In response to these challenges, we construct a fine-grained smartphone usage behavior dataset containing 3,664,325 clicks of 100 users and propose a UI element spatial hierarchy aware smartphone user click behavior prediction method (SHA-SCP). SHA-SCP builds element groups by clustering the elements according to their spatial positions and uses attention mechanisms to perceive the UI at the element level and the element group level to fully capture the information of different scales. Experiments are conducted on the fine-grained smartphone usage behavior dataset, and the results show that our method outperforms the best baseline by an average of 10.52%, 11.34%, and 10.42% in Top-1 Accuracy, Top-3 Accuracy, and Top-5 Accuracy, respectively

Advances in the Isolation of Specific Monoclonal Rabbit Antibodies

The rabbit monoclonal antibodies (mAbs) have advantages in pharmaceuticals and diagnostics with high affinity and specificity. During the past decade, many techniques have been developed for isolating rabbit mAbs, including single B cell antibody technologies. This review describes the basic characterization of rabbit antibody repertoire and summarizes methods of hybridoma technologies, phage display platform, and single B cell antibody technologies. With advances in antibody function and repertoire analysis, rabbit mAbs will be widely used in therapeutic applications in the coming years

Comparative study of differentiating human pluripotent stem cells into vascular smooth muscle cells in hydrogel-based culture methods

Vascular smooth muscle cells (VSMCs), which provides structural integrity and regulates the diameter of vasculature, are of great potential for modeling vascular-associated diseases and tissue engineering. Here, we presented a detailed comparison of differentiating human pluripotent stem cells (hPSCs) into VSMCs (hPSCs-VSMCs) in four different culture methods, including 2-dimensional (2D) culture, 3-dimensional (3D) PNIPAAm-PEG hydrogel culture, 3-dimensional (3D) alginate hydrogel culture, and transferring 3- dimensional alginate hydrogel culture to 2-dimensional (2D) culture. Both hydrogel-based culture methods could mimic in vivo microenvironment to protect cells from shear force, and avoid cells agglomeration, resulting in the extremely high culture efficiency (e.g., high viability, high purity and high yield) compared with 2D culture. We demonstrated hPSC-VSMCs produced from hydrogel-based culture methods had better contractile phenotypes and the potential of vasculature formation. The transcriptome analysis showed the hPSC-VSMCs derived from hydrogel-based culture methods displayed more upregulated genes in vasculature development, angiogenesis and blood vessel development, extracellular matrix compared with 2D culture. Taken together, hPSC-VSMCs produced from hydrogel-based culture system could be applied in various biomedical fields, and further indicated the suitable development of alginate hydrogel for industrial production by taking all aspects into consideration