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ANCOM-BC2 is developed to perform multigroup differential abundance analysis and allows modeling of covariates and longitudinal measures while controlling false discovery rate (FDR) or mixed directional FDR.
The thermal-plex method for highly multiplexed imaging uses DNA probes activated when briefly elevated to designated temperatures for rapid, fluidics-free sequential imaging in cells and tissues.
In 1858, the first standard for microscope objectives was established to encourage interchangeable components. Over the following 150 years, standards have evolved to constrain the size of objectives, which limits the parameters of working distance, field of view and resolution. A new design breaks out of this conventional envelope, offering an ultra-long working distance in air and enabling new neuroscience experiments.
We have developed a framework for the analysis of multi-batch proteome profiling data using isobaric mass tags. Our framework improves quantitative accuracy and increases statistical power by accounting for known sources of variation between batches, thus enabling multiplexed proteome profiling analysis to be performed on large numbers of samples and population cohorts.
The Cousa objective is an ultra-long working distance air objective optimized for two- and three-photon imaging. Bypassing challenges caused by water immersion and short working distances, the Cousa enables and improves imaging of diverse specimens.
Two studies show that nanopores can identify the 20 proteinogenic amino acids and some of their post-translational modifications. Coupled with an exopeptidase, a bottom-up approach to protein sequencing using nanopores is on the horizon.
Serial Lift-Out creates a series of lamellae from one lift-out volume for cryo-ET, increasing the ease and throughput of cryo-lift-out and enabling the study of molecular anatomy in multicellular systems including C. elegans larvae.
How accurate is the prediction of protein structure by AlphaFold? Terwilliger et al. address this question with a rigorous assessment of the accuracy of AlphaFold-predicted structures by comparing them with experimentally determined X-ray crystallographic data.
DeepMainmast is a protein structure modeling protocol for cryo-EM that combines the strengths of a deep-learning-based de novo protein main-chain-tracing approach with AlphaFold2-based structure predictions for improved performance.