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Single-cell RNA sequencing (scRNA-seq) enables high-resolution analysis of cellular heterogeneity, but dropout events, where gene expression is undetected in individual cells, present a significant challenge. We propose \textbf{scMASKGAN}, which transforms matrix imputation into a pixel restoration task to improve the recovery of missing gene expression data.  Specifically, we integrate masking, convolutional neural networks (CNNs), attention mechanisms, and residual networks (ResNets) to effectively address dropout events in scRNA-seq data.

Analysis of bacterial biofilms is particularly challenging and important with diverse applications from systems biology to biotechnology.  Among the variety of techniques that have been applied, time-of-flight secondary ion mass spectrometry (ToF-SIMS) has many promising features in studying the surface characteristics of biofilms. ToF-SIMS offers high spatial resolution and high mass accuracy, which permit surface sensitive analysis of biofilm components. Thus, ToF-SIMS provides a powerful solution to addressing the challenge of bacterial biofilm analysis.