An open-source software tool enables the anatomical parcellation of an unprecedented number of subcortical structures in magnetic resonance images of the human brain, automatically and in individual subjects.
The DIAMonDS can automatically and sequentially identify time points of multiple life cycle events such as pupariation, eclosion, and death in individual flies at high temporal resolution and large scale.
Animals work in a world full of surprises, where using energy to position sensors proportional to the location's expected information avoids the pitfalls of positioning them at the information maxima.
Ten popular spike sorting codes are reproducibly benchmarked for accuracy on electrophysiology datasets from eleven laboratories with interactive web-based exploration of thousands of ground-truth units.
Open-source software can untwist images of live Caenorhabditis elegans embryos, allowing epidermal and neuronal cell position and morphology to be examined in previously inaccessible developmental time periods.
A new automated and unsupervised algorithm, Risk Assessment Population IDentification, identifies risk-stratifying cells in single cell datasets with robust statistical and biological validation.
A novel method predicts cancer and immune cell types from bulk tumor gene expression data with the ability to consider uncharacterized and possibly highly variable cell types, which is validated in human genome.
A dual-channel image registration pipeline combined with deep-learning inference achieves accurate-and-flexible registration/segmentation/mapping of mouse brain.
New reconstruction methods are used to create a publicly available dense reconstruction of the neurons and chemical synapses of central brain of Drosophila, with analysis of its graph properties.
A new analysis algorithm (DISC) enables accurate analysis of data from high-throughput single-molecule paradigms and reveals a non-cooperative binding mechanism of cyclic nucleotide-binding domains from HCN ion channels.
