Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning | Nature Biotechnology
A principal challenge in the analysis of tissue imaging data is cell segmentation—the task of identifying the precise boundary of every cell in an image. To address this problem researchers constructed TissueNet, a dataset for training segmentation models that contains more than 1 million manually labeled cells, an order of magnitude more than all previously published segmentation training datasets. They used TissueNet to train Mesmer, a deep-learning-enabled segmentation algorithm. They demonstrated that Mesmer is more accurate than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance.
TDFragMapper: a visualization tool for evaluating experimental parameters in top-down proteomics | Bioinformatics
Researchers present a new software-tool allowing an easy visualization of fragment ions and thus a rapid evaluation of key experimental parameters on the sequence coverage obtained for the MS/MS analysis of intact proteins. This tool can process data obtained from various deconvolution and fragment assignment software.
A nowcasting framework for correcting for reporting delays in malaria surveillance | PLOS Computational Biology
Time lags in reporting to national surveillance systems represent a major barrier for the control of infectious diseases, preventing timely decision making and resource allocation. This issue is particularly acute for infectious diseases like malaria, which often impact rural and remote communities the hardest. Here, researchers analyze 13 years of malaria surveillance data, identifying key correlates of time lags between clinical cases occurring and being added to the central data system. They develop nowcasting methods that use historical patterns of reporting delays to estimate occurred-but-not-reported monthly malaria cases.