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Our understanding of how human embryos develop before gastrulation, including spatial self-organization and cell type ontogeny, remains limited by available two-dimensional technological platforms(1,2) that do not recapitulate the in vivo conditions(3-5). Here we report a three-dimensional (3D) blastocyst-culture system that enables human blastocyst development up to the primitive streak anlage stage. These 3D embryos mimic developmental landmarks and 3D architectures in vivo, including the embryonic disc, amnion, basement membrane, primary and primate unique secondary yolk sac, formation of anterior-posterior polarity and primitive streak anlage. Using single-cell transcriptome profiling, we delineate ontology and regulatory networks that underlie the segregation of epiblast, primitive endoderm and trophoblast. Compared with epiblasts, the amniotic epithelium shows unique and characteristic phenotypes. After implantation, specific pathways and transcription factors trigger the differentiation of cytotrophoblasts, extravillous cytotrophoblasts and syncytiotrophoblasts. Epiblasts undergo a transition to pluripotency upon implantation, and the transcriptome of these cells is maintained until the generation of the primitive streak anlage. These developmental processes are driven by different pluripotency factors. Together, findings from our 3D-culture approach help to determine the molecular and morphogenetic developmental landscape that occurs during human embryogenesis.

A 3D culture system to model human embryonic development, together with single-cell transcriptome profiling, provides insights into the molecular developmental landscape during human post-implantation embryogenesis.

Although AI companies market software for recognizing emotions in faces, psychologists debate whether expressions can be read so easily.

Although AI companies market software for recognizing emotions in faces, psychologists debate whether expressions can be read so easily.

A video-based deep learning algorithm-EchoNet-Dynamic-accurately identifies subtle changes in ejection fraction and classifies heart failure with reduced ejection fraction using information from multiple cardiac cycles.

Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular disease(1), screening for cardiotoxicity(2) and decisions regarding the clinical management of patients with a critical illness(3). However, human assessment of cardiac function focuses on a limited sampling of cardiac cycles and has considerable inter-observer variability despite years of training(4,5). Here, to overcome this challenge, we present a video-based deep learning algorithm-EchoNet-Dynamic-that surpasses the performance of human experts in the critical tasks of segmenting the left ventricle, estimating ejection fraction and assessing cardiomyopathy. Trained on echocardiogram videos, our model accurately segments the left ventricle with a Dice similarity coefficient of 0.92, predicts ejection fraction with a mean absolute error of 4.1% and reliably classifies heart failure with reduced ejection fraction (area under the curve of 0.97). In an external dataset from another healthcare system, EchoNet-Dynamic predicts the ejection fraction with a mean absolute error of 6.0% and classifies heart failure with reduced ejection fraction with an area under the curve of 0.96. Prospective evaluation with repeated human measurements confirms that the model has variance that is comparable to or less than that of human experts. By leveraging information across multiple cardiac cycles, our model can rapidly identify subtle changes in ejection fraction, is more reproducible than human evaluation and lays the foundation for precise diagnosis of cardiovascular disease in real time. As a resource to promote further innovation, we also make publicly available a large dataset of 10,030 annotated echocardiogram videos.

CRISPR-Cas systems cannot eliminate temperate bacteriophages from bacterial populations and-in this context-the systems impose immunopathological costs on the host, creating selective pressures that may explain their patchy distribution in bacteria.