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| http://dx.doi.org/10.1186/1749-7922-1-1 | DOI Listing |
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Marigold: a machine learning-based web app for zebrafish pose tracking.
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Biology Department, University of Massachusetts Amherst, Amherst, MA, USA.
Background: High-throughput behavioral analysis is important for drug discovery, toxicological studies, and the modeling of neurological disorders such as autism and epilepsy. Zebrafish embryos and larvae are ideal for such applications because they are spawned in large clutches, develop rapidly, feature a relatively simple nervous system, and have orthologs to many human disease genes. However, existing software for video-based behavioral analysis can be incompatible with recordings that contain dynamic backgrounds or foreign objects, lack support for multiwell formats, require expensive hardware, and/or demand considerable programming expertise.
View Article and Find Full Text PDFCorrecting scale distortion in RNA sequencing data.
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Department of Radiology, University of Minnesota, Minneapolis, MN, 55455, USA.
RNA sequencing (RNA-seq) is the conventional genome-scale approach used to capture the expression levels of all detectable genes in a biological sample. This is now regularly used for population-based studies designed to identify genetic determinants of various diseases. Naturally, the accuracy of these tests should be verified and improved if possible.
View Article and Find Full Text PDFThe interaction network and potential clinical effectiveness of dimensional psychopathology phenotyping based on EMR: a Bayesian network approach.
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The current DSM-oriented diagnostic paradigm has introduced the issue of heterogeneity, as it fails to account for the identification of the neurological processes underlying mental illnesses, which affects the precision of treatment. The Research Domain Criteria (RDoC) framework serves as a recognized approach to addressing this heterogeneity, and several assessment and translation techniques have been proposed. Among these methods, transforming RDoC scores from electronic medical records (EMR) using Natural Language Processing (NLP) has emerged as a suitable technique, demonstrating clinical effectiveness.
View Article and Find Full Text PDFArtificial intelligence methods applied to longitudinal data from electronic health records for prediction of cancer: a scoping review.
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Background: Early detection and diagnosis of cancer are vital to improving outcomes for patients. Artificial intelligence (AI) models have shown promise in the early detection and diagnosis of cancer, but there is limited evidence on methods that fully exploit the longitudinal data stored within electronic health records (EHRs). This review aims to summarise methods currently utilised for prediction of cancer from longitudinal data and provides recommendations on how such models should be developed.
View Article and Find Full Text PDFTime-resolved momentum microscopy with fs-XUV photons at high repetition rates with flexible energy and time resolution.
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Department of Physics, TU Dortmund University, Otto-Hahn-Straße 4, 44227, Dortmund, Germany.
Time-resolved momentum microscopy is an emerging technique based on photoelectron spectroscopy for characterizing ultrafast electron dynamics and the out-of-equilibrium electronic structure of materials in the entire Brillouin zone with high efficiency. In this article, we introduce a setup for time-resolved momentum microscopy based on an energy-filtered momentum microscope coupled to a custom-made high-harmonic generation photon source driven by a multi-100 kHz commercial Yb-ultrafast laser that delivers fs pulses in the extreme ultraviolet range. The laser setup includes a nonlinear pulse compression stage employing spectral broadening in a Herriott-type bulk-based multi-pass cell.
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