Isomerization of N-Allyl Amides To Form Geometrically Defined Di-, Tri-, and Tetrasubstituted Enamides
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
2017; 139 (14): 5133-5139
Enamides represent bioactive pharmacophores in various natural products, and have become increasingly common reagents for asymmetric incorporation of nitrogen functionality. Yet the synthesis of the requisite geometrically defined enamides remains problematic, especially for highly substituted and Z-enamides. Herein we wish to report a general atom economic method for the isomerization of a broad range of N-allyl amides to form Z-di-, tri-, and tetrasubstituted enamides with exceptional geometric selectivity. This report represents the first examples of a catalytic isomerization of N-allyl amides to form nonpropenyl disubstituted, tri- and tetrasubstituted enamides with excellent geometric control. Applications of these geometrically defined enamides toward the synthesis of cis vicinal amino alcohols and tetrasubstituted α-borylamido complexes are discussed.
View details for DOI 10.1021/jacs.7b00564
View details for Web of Science ID 000399353800028
View details for PubMedID 28252296
Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments.
PLoS computational biology
2016; 12 (11)
Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domains of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems.
View details for DOI 10.1371/journal.pcbi.1005177
View details for PubMedID 27814364
View details for PubMedCentralID PMC5096676