Holography is a promising approach to recreate lifelike 3D scenes. However, due to the current Spatial Light Modulators (SLMs) lacking sufficient pixels, the defocused planes of holograms always exhibit obvious interference phenomena. The methods based on random phase can alleviate this problem, but they always affect the imaging quality of the focal plane. Meanwhile, direct current (DC) noise of nondiffracted light in SLMs, coupled with ubiquitous dynamic noise, has long been a fundamental issue affecting holographic display quality. In this study, we proposed a method based on static high-pass filtering and time-multiplexing that overcomes the traditional tradeoff between divergence capability of holograms and display quality in focal planes. Simultaneously, the proposed method can eliminate DC and dynamic noise with a simple and robust structure. Moreover, we further extended artificial intelligence-driven algorithms to achieve higher-quality on-axis amplitude-only holograms. The Simulations and experiments demonstrated that the developed method is a promising and easily generalized solution for time-multiplexing holography.

MOTIVATION: Virus integration in the host genome is frequently reported to be closely associated with many human diseases, and the detection of virus integration is a critically challenging task. However, most existing tools show limited specificity and sensitivity. Therefore, the objective of this study is to develop a method for accurate detection of virus integration into host genomes. RESULTS: Herein, we report a novel method termed HIVID2 that is a significant upgrade of HIVID. HIVID2 performs a paired-end combination (PE-combination) for potentially integrated reads. The resulting sequences are then remapped onto the reference genomes, and both split and discordant chimeric reads are used to identify accurate integration breakpoints with high confidence. HIVID2 represents a great improvement in specificity and sensitivity, and predicts breakpoints closer to the real integrations, compared with existing methods. The advantage of our method was demonstrated using both simulated and real datasets. HIVID2 uncovered novel integration breakpoints in well-known cervical cancer-related genes, including FHIT and LRP1B, which was verified using protein expression data. In addition, HIVID2 allows the user to decide whether to automatically perform advanced analysis using the identified virus integrations. By analyzing the simulated data and real data tests, we demonstrated that HIVID2 is not only more accurate than HIVID but also better than other existing programs with respect to both sensitivity and specificity. We believe that HIVID2 will help in enhancing future research associated with virus integration. AVAILABILITYAND IMPLEMENTATION: HIVID2 can be accessed at https://github.com/zengxi-hada/HIVID2/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.