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SyConn2 is a machine learning-based framework for inferring and analyzing the connectomes contained in a volume electron microscopy dataset of brain tissue, for example from the zebra finch.
ClampFISH 2.0 enables highly specific multiplexed signal amplification in RNA FISH. The approach was used to detect 10 RNA species that ranged in abundance in more than 1 million cells and is also applicable to tissue sections.
This paper presents an iterative procedure where AlphaFold models are automatically rebuilt on the basis of experimental density maps and the rebuilt models are used as templates in new AlphaFold predictions.
Omnipose is a deep neural network algorithm for image segmentation that improves upon existing approaches by solving the challenging problem of accurately segmenting morphologically diverse cells from images acquired with any modality.
Light-Seq uses light-directed DNA barcoding in fixed cells and tissues for multiplexed spatial indexing and subsequent next generation sequencing. This approach blends spatial and omics information to enable analysis of rare cell types in complex tissues.
The EternaBench dataset of synthetic RNA constructs was used to directly compare RNA secondary structure prediction software packages on ensemble-oriented prediction tasks and used to train the EternaFold model for improved performance.
A machine learning competition results in tools for labeling protein patterns of single cells in images with population labels. The winners improve the state of the art and provide strategies to deal with weak classification challenges.
TracX improves the accuracy of single-cell tracking by using a fingerprinting approach to measure the similarity between cells in two consecutive images. The approach is applicable across modalities and enables biological discovery.
A fluorescent sensor for oxytocin called MTRIAOT has been developed. The sensor’s capabilities are demonstrated in fiber photometry measurements in freely behaving mice.
SVision is a deep-learning-based method that can sensitively and accurately detect and characterize complex structural variants using long-read sequencing data.
Event-triggered STED is an automated approach that can initiate 2D or 3D STED imaging of specific regions in biological samples after detection of an event of interest. This approach can help maximize observations in live cell imaging and enable discovery.
Event-driven acquisition uses neural-network-based recognition of specific biological events to trigger switching between slow and fast super-resolution imaging, enriching the capture of interesting events with high spatiotemporal resolution.
MIRA facilitates accurate inference of cell state trees and regulatory mechanisms driving cell fate decisions using single-cell multimodal data profiling gene expression and chromatin accessibility.