The human disease methylation database (DiseaseMeth, http://bioinfo.hrbmu.edu.cn/diseasemeth/) is an interactive database that aims to present the most complete collection and annotation of aberrant DNA methylation in human diseases, especially various cancers. Recently, the high-throughput microarray and sequencing technologies have promoted the production of methylome data that contain comprehensive knowledge of human diseases. In this DiseaseMeth update, we have increased the number of samples from 3610 to 32 701, the number of diseases from 72 to 88 and the disease–gene associations from 216 201 to 679 602. DiseaseMeth version 2.0 provides an expanded comprehensive list of disease–gene associations based on manual curation from experimental studies and computational identification from high-throughput methylome data. Besides the data expansion, we also updated the search engine and visualization tools. In particular, we enhanced the differential analysis tools, which now enable online automated identification of DNA methylation abnormalities in human disease in a case-control or disease–disease manner. To facilitate further mining of the disease methylome, three new web tools were developed for cluster analysis, functional annotation and survival analysis. DiseaseMeth version 2.0 should be a useful resource platform for further understanding the molecular mechanisms of human diseases.

Please cite this article as: RRH: Impact of COVID-19 quarantine on myopia development This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

With no requirements for lattice matching, van der Waals (vdW) ferromagnetic materials are rapidly establishing themselves as effective building blocks for next-generation spintronic devices. We report a hitherto rarely seen antisymmetric magnetoresistance (MR) effect in vdW heterostructured Fe3GeTe2 (FGT)/graphite/FGT devices. Unlike conventional giant MR (GMR), which is characterized by two resistance states, the MR in these vdW heterostructures features distinct high-, intermediate-, and low-resistance states. This unique characteristic is suggestive of underlying physical mechanisms that differ from those observed before. After theoretical calculations, the three-resistance behavior was attributed to a spin momentum locking induced spin-polarized current at the graphite/FGT interface. Our work reveals that ferromagnetic heterostructures assembled from vdW materials can exhibit substantially different properties to those exhibited by similar heterostructures grown in vacuum. Hence, it highlights the potential for new physics and new spintronic applications to be discovered using vdW heterostructures.

Highlights d BEACON induces basal levels of DNA breaks and DNA damage response d BEACON induces a basal level of RNA off-target mutations d BEACON induces in vivo base editing with high product purity

Background Understanding the host genetic architecture and viral immunity contributes to the development of effective vaccines and therapeutics for controlling the COVID-19 pandemic. Alterations of immune responses in peripheral blood mononuclear cells play a crucial role in the detrimental progression of COVID-19. However, the effects of host genetic factors on immune responses for severe COVID-19 remain largely unknown. Methods We constructed a computational framework to characterize the host genetics that influence immune cell subpopulations for severe COVID-19 by integrating GWAS summary statistics (N = 969,689 samples) with four independent scRNA-seq datasets containing healthy controls and patients with mild, moderate, and severe symptom (N = 606,534 cells). We collected 10 predefined gene sets including inflammatory and cytokine genes to calculate cell state score for evaluating the immunological features of individual immune cells. Results We found that 34 risk genes were significantly associated with severe COVID-19, and the number of highly expressed genes increased with the severity of COVID-19. Three cell subtypes that are CD16+monocytes, megakaryocytes, and memory CD8+T cells were significantly enriched by COVID-19-related genetic association signals. Notably, three causal risk genes of CCR1, CXCR6, and ABO were highly expressed in these three cell types, respectively. CCR1+CD16+monocytes and ABO+ megakaryocytes with significantly up-regulated genes, including S100A12, S100A8, S100A9, and IFITM1, confer higher risk to the dysregulated immune response among severe patients. CXCR6+ memory CD8+ T cells exhibit a notable polyfunctionality including elevation of proliferation, migration, and chemotaxis. Moreover, we observed an increase in cell-cell interactions of both CCR1+ CD16+monocytes and CXCR6+ memory CD8+T cells in severe patients compared to normal controls among both PBMCs and lung tissues. The enhanced interactions of CXCR6+ memory CD8+T cells with epithelial cells facilitate the recruitment of this specific population of T cells to airways, promoting CD8+T cell-mediated immunity against COVID-19 infection. Conclusions We uncover a major genetics-modulated immunological shift between mild and severe infection, including an elevated expression of genetics-risk genes, increase in inflammatory cytokines, and of functional immune cell subsets aggravating disease severity, which provides novel insights into parsing the host genetic determinants that influence peripheral immune cells in severe COVID-19.

A variety of base editors have been developed to achieve C-toT editing in different genomic contexts. Here, we compare a panel of five base editors on their C-toT editing efficiencies and product purity at commonly editable sites, including some human pathogenic C-toT mutations. We further profile the accessibilities of 20 base editors to all possible pathogenic mutations in silico. Finally, we build the BEable-GPS (Base Editable prediction of Global Pathogenic SNVs) database for users to select proper base editors to model or correct disease-related mutations. The in vivo comparison and in silico profiling catalog the availability of base editors and their broad applications in biomedical studies.

A graded refractive index silicon oxynitride (SiO,N,) thin film was prepared on a silicon substrate by ion assisted deposition. Spectroscopic ellipsometry (SE) was used to optically analyze the film. The measured SE spectra (2500-8200 A) were analyzed with several fitting models, whose construction was based on an Auger depth profile of the film. In each model, the optical response of SiO,N, was described using the Bruggeman effective medium approximation, by modeling it as a physical mixture of two distinct phases: silicon dioxide and silicon nitride. Grading n7as modeled by varying the silicon nitride volume fraction with depth below the surface, according to an assumed profile. Fitting results were very sensitive to the profile chosen, which was different for each model. Experimentation with the profile led to a model which produced a remarkably good fit, over the entire spectral range. As a result, the film thickness and its refractive index profile were determined. The index profile determined by SE analysis was found to be consistent with the Auger profile.

No abstract