Publications by authors named "P J W Baldwin"
Neurostimulation protocols are increasingly used as therapeutic interventions, including for brain injury. In addition to the direct activation of neurons, these stimulation protocols are also likely to have downstream effects on those neurons' synaptic outputs. It is well known that alterations in the strength of synaptic connections (long-term potentiation, LTP; long-term depression, LTD) are sensitive to the frequency of stimulation used for induction; however, little is known about the contribution of the temporal pattern of stimulation to the downstream synaptic plasticity that may be induced by neurostimulation in the injured brain.
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- Cryogenic electron tomography (cryoET) is an advanced imaging technique that captures detailed 3D images of biological specimens but struggles with data collection limitations like the missing wedge problem.
- Recent advancements using supervised deep learning methods, particularly convolutional neural networks (CNNs), have helped improve cryoET quality but require substantial pretraining, which can lead to inaccuracies when training data is limited.
- To address these issues, a new unsupervised learning approach using coordinate networks (CNs) has been proposed, significantly speeding up reconstruction times and enhancing image quality without needing pretraining, as demonstrated by improved shape completion and fewer artifacts in experimental results.
Article Synopsis
- - Cryogenic electron tomography (cryoET) provides high-resolution 3D imaging of biological samples, but it struggles with the "missing wedge" problem which affects data quality due to limited collection angles.
- - Recent advancements in supervised deep learning, particularly convolutional neural networks (CNNs), have improved cryoET but often rely heavily on pretraining, which can lead to errors when training data is limited.
- - The proposed unsupervised learning method using coordinate networks (CNs) eliminates the need for pretraining, significantly speeds up reconstruction times, and improves image quality by reducing artifacts, offering insights on both supervised and unsupervised learning for better cryoET methods.
Article Synopsis
- In January 2020, a workshop at EMBL-EBI focused on data needs for cryoEM structure deposition and validation, specifically in single-particle analysis.
- The workshop gathered 47 experts to discuss data processing, model building, validation, and archiving, leading to consensus recommendations.
- The report outlines the workshop's goals, key discussions, challenges for future methods, and the progress made on implementing the recommendations.
Background: The coronavirus disease 2019 (COVID-19) pandemic has presented immense challenges to health systems worldwide and significantly impacted the mental health of frontline healthcare workers.
Aims: This study drew on the experiences of frontline healthcare workers to examine organizational strategies needed to support the mental health and well-being of healthcare workers during times of crisis.
Methods: Semi-structured focus groups or individual interviews were conducted with healthcare workers to examine their perspectives on organizational strategies for enhancing staff mental health and well-being during crises.