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.
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 non-propenyl disubstituted, tri- and tetrasubstituted enamides with excellent geometric control. Applications of these geometrically defined enamides towards the synthesis of cis vicinal amino alcohols and tetrasubstituted α-borylamido complexes are discussed.
Despite progressive improvements over the decades, the rich temporally resolved data in an echocardiogram remain underutilized. Human assessments reduce the complex patterns of cardiac wall motion, to a small list of measurements of heart function. All modern echocardiography artificial intelligence (AI) systems are similarly limited by design – automating measurements of the same reductionist metrics rather than utilizing the embedded wealth of data. This underutilization is most evident where clinical decision making is guided by subjective assessments of disease acuity. Predicting the likelihood of developing post-operative right ventricular failure (RV failure) in the setting of mechanical circulatory support is one such example. Here we describe a video AI system trained to predict post-operative RV failure using the full spatiotemporal density of information in pre-operative echocardiography. We achieve an AUC of 0.729, and show that this ML system significantly outperforms a team of human experts at the same task on independent evaluation.
Aortic thrombus can be rare, requiring prompt recognition and management to prevent sequelae. Treatment modalities for aortic thrombus include systemic anticoagulation, endovascular, and/or surgical intervention. We present an incidental finding of an aortic annular mass in a 53-year-old male consistent with an aortic thrombus. (
Level of Difficulty: Intermediate.
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