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FLIRT enables spatiotemporally precise control of protein function in C. elegans by harnessing existing temperature-sensitive mutations. Proteins can be inactivated at desired sites by infrared laser light targeted to the region(s) of interest.
A software tool, emClarity, implements several improvements in cryo-electron tomography image-processing algorithms to achieve sub-nanometer resolution for diverse macromolecular structures.
The combination of transparency, small brain size and genetic access positions Danionella translucida as a promising model organism for functional imaging of neuronal circuits, especially during complex behaviors in adults.
Guide Swap challenges the hypothesis that Cas9–sgRNA binding is irreversible. The authors find that instead, nontargeting sgRNAs are swapped for targeting sgRNAs in the Cas9 complex. The method allows genome-scale functional screens in primary cells
The Qiita web platform provides access to large amounts of public microbial multi-omic data and enables easy analysis and meta-analysis of standardized private and public data.
The synOptopatch approach enables all-optical access to synaptic communication via mutually exclusive expression of an optogenetic actuator and a voltage sensor in pre- and postsynaptic neurons, respectively.
A charge-integrating pixel-array detector called JUNGFRAU enables the collection of highly accurate X-ray crystallography data at synchrotron sources at unprecedented speeds.
A computational and analytical framework enables multicolor 3D particle reconstruction of protein complexes from 2D images. The authors demonstrate the power of the approach by reconstructing native proteins within the human centriole.
Reducing the length of time that protein particles spend on a sample grid prior to freezing mitigates deleterious effects caused by particle adsorption to the air–water interface in single-particle cryo-EM.
LFADS, a deep learning method for analyzing neural population activity, can extract neural dynamics from single-trial recordings, stitch separate datasets into a single model, and infer perturbations, for example, from behavioral choices to these dynamics.
Convolutional neural networks enable prediction of fluorescently labeled structures from three-dimensional time-lapse transmitted-light images. Applications include multiplexed long time-lapse imaging and prediction of fluorescence in electron micrographs.
An all-to-all registration approach allows for improved, high-resolution, template-free single-particle reconstruction from localization microscopy data under realistic experimental conditions such as low labeling density.
Wang et al. demonstrate that the effects of aberrations and scattering caused by the mouse skull can be reduced with three-photon microscopy. Their approach allows structural and functional imaging of the brain through an intact skull.
CDeep3M provides a user-friendly tool for deep-learning-based image segmentation via a cloud-based deep convolutional neural network. Demonstrations include challenging light, X-ray, and electron microscopy segmentation tasks.
REVOLVER uses transfer learning on multi-region tumor sequencing data to jointly infer tumor evolution models in multiple individuals and to detect repeated evolutionary trajectories. Repeated evolution can be used to stratify the cohort.