GraphCpG: imputation of single-cell methylomes based on locus-aware neighboring subgraphs.

Motivation: Single-cell DNA methylation sequencing can assay DNA methylation at single-cell resolution. However, incomplete coverage compromises related downstream analyses, outlining the importance of imputation techniques. With a rising number of cell samples in recent large datasets, scalable and efficient imputation models are critical to addressing the sparsity for genome-wide analyses.

High-Precision Wafer Bonding Alignment Mark Using Moiré Fringes and Digital Grating.

This paper investigates a moiré-based mark for high-precision wafer bonding alignment. During alignment, the mark is combined with digital grating, which has the benefits of high precision and small size. A digital grating is superimposed on the mark to generate moiré fringes.

scMelody: An Enhanced Consensus-Based Clustering Model for Single-Cell Methylation Data by Reconstructing Cell-to-Cell Similarity.

Single-cell DNA methylation sequencing technology has brought new perspectives to investigate epigenetic heterogeneity, supporting a need for computational methods to cluster cells based on single-cell methylation profiles. Although several methods have been developed, most of them cluster cells based on single (dis)similarity measures, failing to capture complete cell heterogeneity and resulting in locally optimal solutions. Here, we present scMelody, which utilizes an enhanced consensus-based clustering model to reconstruct cell-to-cell methylation similarity patterns and identifies cell subpopulations with the leveraged information from multiple basic similarity measures.

CaMelia: imputation in single-cell methylomes based on local similarities between cells.

Motivation: Single-cell DNA methylation sequencing detects methylation levels with single-cell resolution, while this technology is upgrading our understanding of the regulation of gene expression through epigenetic modifications. Meanwhile, almost all current technologies suffer from the inherent problem of detecting low coverage of the number of CpGs. Therefore, addressing the inherent sparsity of raw data is essential for quantitative analysis of the whole genome.

Adaptive boundary control of a vibrating cantilever nanobeam considering small scale effects.

This paper presents vibration control analysis for a cantilever nanobeam system. The dynamics of the system is obtained by the non-local elastic relationship which characterizes the small scale effects. The boundary conditions and governing equation are respectively expressed by several ordinary differential equations (ODE) and a partial differential equation (PDE) with the help of the Hamilton's principle.

PretiMeth: precise prediction models for DNA methylation based on single methylation mark.

Background: The computational prediction of methylation levels at single CpG resolution is promising to explore the methylation levels of CpGs uncovered by existing array techniques, especially for the 450 K beadchip array data with huge reserves. General prediction models concentrate on improving the overall prediction accuracy for the bulk of CpG loci while neglecting whether each locus is precisely predicted. This leads to the limited application of the prediction results, especially when performing downstream analysis with high precision requirements.

Bayesian Estimation of Human Impedance and Motion Intention for Human-Robot Collaboration.

This article proposes a Bayesian method to acquire the estimation of human impedance and motion intention in a human-robot collaborative task. Combining with the prior knowledge of human stiffness, estimated stiffness obeying Gaussian distribution is obtained by Bayesian estimation, and human motion intention can be also estimated. An adaptive impedance control strategy is employed to track a target impedance model and neural networks are used to compensate for uncertainties in robotic dynamics.

A Hybrid Ensemble Approach for Identifying Robust Differentially Methylated Loci in Pan-Cancers.

DNA methylation is a widely investigated epigenetic mark that plays a vital role in tumorigenesis. Advancements in high-throughput assays, such as the Infinium 450K platform, provide genome-scale DNA methylation landscapes in single-CpG locus resolution, and the identification of differentially methylated loci has become an insightful approach to deepen our understanding of cancers. However, the situation with extremely unbalanced numbers of samples and loci (approximately 1:1,000) makes it rather difficult to explore differential methylation between the sick and the normal.

MRCNN: a deep learning model for regression of genome-wide DNA methylation.

Background: Determination of genome-wide DNA methylation is significant for both basic research and drug development. As a key epigenetic modification, this biochemical process can modulate gene expression to influence the cell differentiation which can possibly lead to cancer. Due to the involuted biochemical mechanism of DNA methylation, obtaining a precise prediction is a considerably tough challenge.