We documented the relabeling process for a subset of a renowned database for emotion-in-context recognition, with the aim of promoting reliability in final labels. To this end, emotion categories were organized into eight groups, while a large number of participants was requested for tagging. A strict control strategy was performed along the experiments, whose duration was 13.45 minutes average per day. Annotators were free to participate in any of the daily experiments (the average number of participants was 28), and a Z-Score filtering technique was implemented to keep trustworthiness of annotations. As a result, the value of the agreement parameter Fleiss' Kapa increasingly varied from slight to almost perfect, revealing a coherent diversity of the experiments. Our results support the hypothesis that a small number of categories and a large number of voters benefit reliability of annotations in contextual emotion imagery.
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http://dx.doi.org/10.1038/s41597-023-02435-1 | DOI Listing |
Tomography
January 2025
Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
Objectives: Accurate kidney and tumor segmentation of computed tomography (CT) scans is vital for diagnosis and treatment, but manual methods are time-consuming and inconsistent, highlighting the value of AI automation. This study develops a fully automated AI model using vision transformers (ViTs) and convolutional neural networks (CNNs) to detect and segment kidneys and kidney tumors in Contrast-Enhanced (CECT) scans, with a focus on improving sensitivity for small, indistinct tumors.
Methods: The segmentation framework employs a ViT-based model for the kidney organ, followed by a 3D UNet model with enhanced connections and attention mechanisms for tumor detection and segmentation.
J Pharm Anal
October 2024
Department of Biosciences and Medical Biology, Bioanalytical Research Labs, University of Salzburg, Salzburg, 5020, Austria.
Glycans associated with biopharmaceutical drugs play crucial roles in drug safety and efficacy, and therefore, their reliable detection and quantification is essential. Our study introduces a multi-level quantification approach for glycosylation analysis in monoclonal antibodies (mAbs), focusing on minor abundant glycovariants. Mass spectrometric data is evaluated mainly employing open-source software tools.
View Article and Find Full Text PDFLow magnesium (Mg) intake increases the risk of various diseases such as anxiety disorder, depression, and diabetes. However, a reliable biomarker of mild Mg deficiency due to low Mg intake has not yet been identified. We speculate that metabolomics will be effective for biomarker discovery because Mg can affect various metabolic processes in the body.
View Article and Find Full Text PDFData Brief
February 2025
Department of Electrical, Electronic and Communication Engineering, Military Institute of Science and Technology (MIST), Dhaka 1216, Bangladesh.
The dataset represents a significant advancement in Bengali lip-reading and visual speech recognition research, poised to drive future applications and technological progress. Despite Bengali's global status as the seventh most spoken language with approximately 265 million speakers, linguistically rich and widely spoken languages like Bengali have been largely overlooked by the research community. fills this gap by offering a pioneering dataset tailored for Bengali lip-reading, comprising visual data from 150 speakers across 54 classes, encompassing Bengali phonemes, alphabets, and symbols.
View Article and Find Full Text PDFExp Biol Med (Maywood)
January 2025
Institute of Clinical Medicine, University Tartu, Tartu, Estonia.
Blood-based biomarkers for motor neuron disease are needed for better diagnosis, progression prediction, and clinical trial monitoring. We used whole blood-derived total RNA and performed whole transcriptome analysis to compare the gene expression profiles in (motor neurone disease) MND patients to the control subjects. We compared 42 MND patients to 42 aged and sex-matched healthy controls and described the whole transcriptome profile characteristic for MND.
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