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Graph neural networks and transformers taking advantage of contextual information and large unannotated multimodal datasets are redefining what is possible in computational medicine.
Comparisons of neural recordings across time, across subsets of neurons and across individuals requires the alignment of low-dimensional latent representations.
Research manuscripts and the associated scientific data generated for projects that are funded by federal agencies in the United States will need to be made publicly available immediately on publication.
Robotic fingers and arms that augment the motor abilities of non-disabled individuals are increasingly feasible yet face neurocognitive barriers and hurdles in efferent motor control.
By allowing for the visualization of living tissue faster, at higher contrast or with larger fields of view, imaging modalities widely used in research are making inroads into the detection of disease in the human body.
Better cell sourcing and increasingly fine control over cell differentiation, tissue formation and cell and tissue maturation are pushing forward progress in disease modelling, drug development and regenerative medicine.
Lessons being learned about the utility of COVID-19 diagnostics are informing the design, required real-world performance and deployment needs of technologies for the detection of infectious diseases.
The growth of citations to published content typically follows an S-shaped curve. We look back at the fairly homogeneous citation-growth patterns — and at the few exceptions to them — for the content that we published in 2017.
The assessment of the performance of molecular assays would benefit from quantifying the resolution of molecular concentration — a new metric that we propose and evaluate here.
A deeper understanding of the myriad ways that the mechanics of cellular and tissue microenvironments trigger or exacerbate disease will open up pathways for new interventions.