Single particle cryo-EM requires full automation to allow high-throughput structure determination. Although software packages exist where parts of the cryo-EM pipeline are automated, a complete solution that offers reliable on-the-fly processing, resulting in high-resolution structures, does not exist. Here we present TranSPHIRE: A software package for fully-automated processing of cryo-EM datasets during data acquisition. TranSPHIRE transfers data from the microscope, automatically applies the common pre-processing steps, picks particles, performs 2D clustering, and 3D refinement parallel to image recording. Importantly, TranSPHIRE introduces a machine learning-based feedback loop to re-train its picking model to adapt to any given data set live during processing. This elegant approach enables TranSPHIRE to process data more effectively, producing high-quality particle stacks. TranSPHIRE collects and displays all metrics and microscope settings to allow users to quickly evaluate data during acquisition. TranSPHIRE can run on a single work station and also includes the automated processing of filaments.
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http://dx.doi.org/10.1038/s41467-020-19513-2 | DOI Listing |
J Phys Chem Lett
January 2025
SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Environmental Theoretical Chemistry, School of Environment, South China Normal University, Guangzhou 510006, China.
Two-dimensional (2D) electronic spectra of the phenylene ethynylene dendrimer with 2-ring and 3-ring branches were evaluated by combining the on-the-fly trajectory surface hopping nonadiabatic dynamics and the doorway-window simulation protocol. The ground state bleach (GSB), stimulated emission (SE), and excited-state absorption (ESA) contributions to the 2D signal were obtained and carefully analyzed. The results demonstrate that the ultrafast intramolecular nonadiabatic excited-state energy transfer (EET) from the 2-ring to the 3-ring units is comprehensively characterized by the SE and ESA signals.
View Article and Find Full Text PDFJ Biomed Opt
January 2025
University of Ljubljana, Faculty of Mathematics and Physics, Ljubljana, Slovenia.
Significance: Machine learning models for the direct extraction of tissue parameters from hyperspectral images have been extensively researched recently, as they represent a faster alternative to the well-known iterative methods such as inverse Monte Carlo and inverse adding-doubling (IAD).
Aim: We aim to develop a Bayesian neural network model for robust prediction of physiological parameters from hyperspectral images.
Approach: We propose a two-component system for extracting physiological parameters from hyperspectral images.
Immersive virtual reality (VR) environments are a powerful tool to explore cognitive processes ranging from memory and navigation to visual processing and decision making-and to do so in a naturalistic yet controlled setting. As such, they have been employed across different species, and by a diverse range of research groups. Unfortunately, designing and implementing behavioral tasks in such environments often proves complicated.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Department of Chemistry, Indian Institute of Technology, Delhi 110016, India.
Enhanced sampling (ES) simulations of biomolecular recognition, such as binding small molecules to proteins and nucleic acid targets, protein-protein association, and protein-nucleic acid interactions, have gained significant attention in the simulation community because of their ability to sample long-time scale processes. However, a key challenge in implementing collective variable (CV)-based enhanced sampling methods is the selection of appropriate CVs that can distinguish the system's metastable states and, when biased, can effectively sample these states. This challenge is particularly acute when the binding of a flexible molecule to a conformationally rich host molecule is simulated, such as the binding of a peptide to an RNA.
View Article and Find Full Text PDFHum Brain Mapp
January 2025
Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland.
State-of-the-art navigated transcranial magnetic stimulation (nTMS) systems can display the TMS coil position relative to the structural magnetic resonance image (MRI) of the subject's brain and calculate the induced electric field. However, the local effect of TMS propagates via the white-matter network to different areas of the brain, and currently there is no commercial or research neuronavigation system that can highlight in real time the brain's structural connections during TMS. This lack of real-time visualization may overlook critical inter-individual differences in brain connectivity and does not provide the opportunity to target brain networks.
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