This study aims to develop a multimodal understanding of transferring an established method of laparoscopy training to the virtual reality domain. The virtual reality version of the laparoscopic box trainer is develop...
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We employed portable optical neuroimaging technology and the Granger causality approach to uncover the impact of the two medical simulation technologies on the directed functional brain network of the subjects with tw...
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Mitral valve (MV) repair is safer than replacement for mitral regurgitation (MR) treatment, but long-term outcomes remain suboptimal and poorly understood. Moreover, preoperative optimization is complicated due to the...
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Discovering a new cancer treatment Researchers are becoming more eager to uncover new medicinal compounds from living species. In the UK, CBD has been rising in demand as an alternative therapeutic agent and is used i...
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Discovering a new cancer treatment Researchers are becoming more eager to uncover new medicinal compounds from living species. In the UK, CBD has been rising in demand as an alternative therapeutic agent and is used in various medicinal products. CBD, or cannabidiol, is a phytocannabinoid, one of the identified cannabinoids in cannabis plants. It doesn't contain tetrahydrocannabinol (THC), which is a psychoactive ingredient. While electroporation is one of the non-invasive techniques for nonviral delivery systems, such as molecule introduction and molecule transfer in a variety of sizes depending on the parameter chosen. The cell response to CBD compound combined with the electroporation method was the focus of this study. The findings demonstrated a positive response to the antiproliferative properties of cancer cells, and the treatment was further aided by a pulse electric field of 600 V/cm for $30 \mu \mathrm{s}$ with a single pulse. The results showed further decrements in cell viability. This finding may contribute toward cancer treatment as an adjunct alternative anticancer agent.
In various cancer types, plant extract has been found to be an important and useful preventive agent for chemotherapy. Cannabidiol is one of the interesting plants that has recently been identified as a possible anti-...
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While mitral valve (MV) repair remains the preferred clinical option for mitral regurgitation (MR) treatment, long-term outcomes remain suboptimal and difficult to predict. Furthermore, pre-operative optimization is c...
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Speech analysis can provide a potential non-invasive and objective means of assessing and monitoring an individual’s mental health. Most studies to date have focused on cross-sectional analysis and have not explored ...
Speech analysis can provide a potential non-invasive and objective means of assessing and monitoring an individual’s mental health. Most studies to date have focused on cross-sectional analysis and have not explored the benefits of speech analysis as a longitudinal monitoring tool that can assist in the management of chronic conditions such as major depressive disorder (MDD). Objectively monitoring for shifts in depression symptom severity levels over time presents a notable challenge, which we address through an automated approach using longitudinal English and Spanish speech samples collected from a clinical population. We employ time–frequency representations and linguistic embeddings to enhance the early recognition of alterations in depression levels in individuals with MDD. We investigate the suitability of using siamese-based training for modeling these changes, intending to enable personalized and adaptive interventions.
This study investigated the effect of pulse electric field (PEF) exposure on cervical cancer cells (HeLa cells) in an in-vitro wound repair model. The study mainly focused on the healing time of HeLa cell line wound m...
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作者:
Ali HashemiYijing GaoChang CaiSanjay GhoshKlaus-Robert MüllerSrikantan S. NagarajanStefan HaufeUncertainty
Inverse Modeling and Machine Learning Group Technische Universität Berlin Germany and Machine Learning Group Technische Universität Berlin Germany Department of Radiology and Biomedical Imaging
University of California San Francisco Department of Radiology and Biomedical Imaging
University of California San Francisco and National Engineering Research Center for E-Learning Central China Normal University China Machine Learning Group
Technische Universität Berlin Germany and BIFOLD – Berlin Institute for the Foundations of Learning and Data Berlin Germany and Department of Artificial Intelligence Korea University South Korea and Max Planck Institute for Informatics Saarbrücken Germany Uncertainty
Inverse Modeling and Machine Learning Group Technische Universität Berlin Germany and Physikalisch-Technische Bundesanstalt Berlin Germany and Charité – Universitätsmedizin Berlin Germany and Bernstein Center for Computational Neuroscience Berlin Germany
Several problems in neuroimaging and beyond require inference on the parameters of multi-task sparse hierarchical regression models. Examples include M/EEG inverse problems, neural encoding models for task-based fMRI ...
ISBN:
(纸本)9781713845393
Several problems in neuroimaging and beyond require inference on the parameters of multi-task sparse hierarchical regression models. Examples include M/EEG inverse problems, neural encoding models for task-based fMRI analyses, and climate science. In these domains, both the model parameters to be inferred and the measurement noise may exhibit a complex spatio-temporal structure. Existing work either neglects the temporal structure or leads to computationally demanding inference schemes. Overcoming these limitations, we devise a novel flexible hierarchical Bayesian framework within which the spatio-temporal dynamics of model parameters and noise are modeled to have Kronecker product covariance structure. Inference in our framework is based on majorization-minimization optimization and has guaranteed convergence properties. Our highly efficient algorithms exploit the intrinsic Riemannian geometry of temporal autocovariance matrices. For stationary dynamics described by Toeplitz matrices, the theory of circulant embeddings is employed. We prove convex bounding properties and derive update rules of the resulting algorithms. On both synthetic and real neural data from M/EEG, we demonstrate that our methods lead to improved performance.
Background: The prosthetic alignment procedure considers biomechanical, anatomical and comfort characteristics of the amputee to achieve an acceptable gait. Prosthetic malalignment induces long-term disease. The asses...
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Background: The prosthetic alignment procedure considers biomechanical, anatomical and comfort characteristics of the amputee to achieve an acceptable gait. Prosthetic malalignment induces long-term disease. The assessment of alignment is highly variable and subjective to the experience of the prosthetist, so the use of machine learning could assist the prosthetist during the judgment of optimal *** objective: To assist the prosthetist during the assessment of prosthetic alignment using a new computational protocol based on machine ***: Sixteen transfemoral amputees were recruited for training and validation of the alignment protocol. Four misalignments and one nominal alignment were performed. Eleven prosthetic limb ground reaction force parameters were recorded. A support vector machine with a Gaussian kernel radial basis function and a Bayesian regularization neural network were trained to predict the alignment condition, as well as the magnitude and angle of required to align the prosthesis correctly. The alignment protocol was validated by one junior and one senior prosthetist during the prosthetic alignment of two transfemoral ***: The support vector machine-based model detected the nominal alignment 92.6% of the time. The neural network recovered 94.11% of the angles needed to correct the prosthetic misalignment with a fitting error of 0.51°. During the validation of the alignment protocol, the computational models and the prosthetists agreed on the alignment assessment. The gait quality evaluated by the prosthetists reached a satisfaction level of 8/10 for the first amputee and 9.6/10 for the second ***: The new computational prosthetic alignment protocol is a tool that helps the prosthetist during the prosthetic alignment procedure thereby decreasing the likelihood of gait deviations and musculoskeletal diseases associated with misalignments and consequently improving the amputees-prosthesis adherence.
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