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Showing: 1–25 of 50

  1. Addressing preferred specimen orientation in single-particle cryo-EM through tilting

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    The preferred specimen orientation problem limits accuracy and resolution in structure determination by cryo-EM. Collecting data at defined sample tilts yielded near-atomic-resolution structures for the influenza hemagglutinin trimer and ribosomal biogenesis intermediates.

  2. Fast high-resolution miniature two-photon microscopy for brain imaging in freely behaving mice

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    FHIRM-TPM is a miniature two-photon microscope capable of imaging fluorescently labeled neurons in the brains of freely behaving mice. It allows for imaging of spines or recording of neural activity with a frame rate up to 40 Hz.

  3. The fickle P value generates irreproducible results

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    The reliability and reproducibility of science are under scrutiny. However, a major cause of this lack of repeatability is not being considered: the wide sample-to-sample variability in the P value. We explain why P is fickle to discourage the ill-informed practice of interpreting analyses based predominantly on this statistic.
  4. mScarlet: a bright monomeric red fluorescent protein for cellular imaging

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    An extremely bright, truly monomeric RFP, mScarlet, is described that outperforms existing RFPs in diverse labeling applications, especially in FRET with ratiometric imaging.

  5. CrY2H-seq: a massively multiplexed assay for deep-coverage interactome mapping

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    CrY2H-seq, a Cre recombinase reporter-mediated yeast two-hybrid method coupled with next-generation sequencing, enables ultra-high-throughput screening of transcription factor interactions in Arabidopsis thaliana.

  6. Video rate volumetric Ca2+ imaging across cortex using seeded iterative demixing (SID) microscopy

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    The seeded iterative demixing strategy, when used in combination with light-field microscopy, enables calcium imaging at single-neuron resolution in the mouse brain at high volumetric imaging rates and depths of up to 380 μm.

  7. Points of Significance: Simple linear regression

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    “The statistician knows...that in nature there never was a normal distribution, there never was a straight line, yet with normal and linear assumptions, known to be false, he can often derive results which match, to a useful approximation, those found in the real world.”