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      A neural hub for holistic courtship displays

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      Current Biology
      Elsevier BV

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          DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

          Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, but markers are intrusive, and the number and location of the markers must be determined a priori. Here we present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. Remarkably, even when only a small number of frames are labeled (~200), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy.
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            Third-generationin situhybridization chain reaction: multiplexed, quantitative, sensitive, versatile, robust

            In situ hybridization based on the mechanism of the hybridization chain reaction (HCR) has addressed multi-decade challenges that impeded imaging of mRNA expression in diverse organisms, offering a unique combination of multiplexing, quantitation, sensitivity, resolution and versatility. Here, with third-generation in situ HCR, we augment these capabilities using probes and amplifiers that combine to provide automatic background suppression throughout the protocol, ensuring that reagents will not generate amplified background even if they bind non-specifically within the sample. Automatic background suppression dramatically enhances performance and robustness, combining the benefits of a higher signal-to-background ratio with the convenience of using unoptimized probe sets for new targets and organisms. In situ HCR v3.0 enables three multiplexed quantitative analysis modes: (1) qHCR imaging – analog mRNA relative quantitation with subcellular resolution in the anatomical context of whole-mount vertebrate embryos; (2) qHCR flow cytometry – analog mRNA relative quantitation for high-throughput expression profiling of mammalian and bacterial cells; and (3) dHCR imaging – digital mRNA absolute quantitation via single-molecule imaging in thick autofluorescent samples. [Related article:] Highlighted Article: In situ hybridization chain reaction (HCR) v3.0 exploits automatic background suppression to enable multiplexed quantitative mRNA imaging and flow cytometry with dramatically enhanced performance and ease of use.
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              Exploitation of Sexual Signals by Predators and Parasitoids

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                Author and article information

                Contributors
                Journal
                Current Biology
                Current Biology
                Elsevier BV
                09609822
                May 2023
                May 2023
                : 33
                : 9
                : 1640-1653.e5
                Article
                10.1016/j.cub.2023.02.072
                36944337
                750e6af1-c59b-4b97-b027-6fb9a4180421
                © 2023

                https://www.elsevier.com/tdm/userlicense/1.0/

                http://www.elsevier.com/open-access/userlicense/1.0/

                https://doi.org/10.15223/policy-017

                https://doi.org/10.15223/policy-037

                https://doi.org/10.15223/policy-012

                https://doi.org/10.15223/policy-029

                https://doi.org/10.15223/policy-004

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