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Biomedical named entity recognition using improved green anaconda-assisted Bi-GRU-based hierarchical ResNet model.
BMC Bioinformatics
January 2025
Department of Information Technology, Vardhaman College of Engineering, Shamshabad, Hyderabad, India.
Background: Biomedical text mining is a technique that extracts essential information from scientific articles using named entity recognition (NER). Traditional NER methods rely on dictionaries, rules, or curated corpora, which may not always be accessible. To overcome these challenges, deep learning (DL) methods have emerged.
View Article and Find Full Text PDFIdentifying Digital Markers of Attention-Deficit/Hyperactivity Disorder (ADHD) in a Remote Monitoring Setting: Prospective Observational Study.
JMIR Form Res
January 2025
Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
Background: The symptoms and associated characteristics of attention-deficit/hyperactivity disorder (ADHD) are typically assessed in person at a clinic or in a research lab. Mobile health offers a new approach to obtaining additional passively and continuously measured real-world behavioral data. Using our new ADHD remote technology (ART) system, based on the Remote Assessment of Disease and Relapses (RADAR)-base platform, we explore novel digital markers for their potential to identify behavioral patterns associated with ADHD.
View Article and Find Full Text PDFBiomarker analysis from complex biofluids by an on-chip chemically modified light-controlled vertical nanopillar array device.
Nat Protoc
January 2025
Center for Nanoscience and Nanotechnology, Tel Aviv University, Tel Aviv-Yafo, Israel.
Nanostructured devices have proven useful in a broad range of applications, from diagnosing diseases to discovering and screening new drug molecules. We developed vertical silicon nanopillar (SiNP) arrays for on-chip multiplex capture of selected biomolecules using a light-induced release of the array's selectively captured biomarkers. This platform allows the rapid, reusable and quantitative capture and release of a selection of biomarkers, followed by their downstream analysis.
View Article and Find Full Text PDFVersatile waste sorting in small batch and flexible manufacturing industries using deep learning techniques.
Sci Rep
January 2025
School of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, Belgrade, Serbia.
The expansion of LEAN and small batch manufacturing demands flexible automated workstations capable of switching between sorting various wastes over time. To address this challenge, our study is focused on assessing the ability of the Segment Anything Model (SAM) family of deep learning architectures to separate highly variable objects during robotic waste sorting. The proposed two-step procedure for generic versatile visual waste sorting is based on the SAM architectures (original SAM, FastSAM, MobileSAMv2, and EfficientSAM) for waste object extraction from raw images, and the use of classification architecture (MobileNetV2, VGG19, Dense-Net, Squeeze-Net, ResNet, and Inception-v3) for accurate waste sorting.
View Article and Find Full Text PDFAbscission in plants: from mechanism to applications.
Adv Biotechnol (Singap)
August 2024
School of Agriculture and Biotechnology, Sun Yat-sen University, Shenzhen, 518107, China.
Abscission refers to the natural separation of plant structures from their parent plants, regulated by external environmental signals or internal factors such as stress and aging. It is an advantageous process as it enables plants to shed unwanted organs, thereby regulating nutrient allocation and ensuring the dispersal of fruits and seeds from the parent. However, in agriculture and horticulture, abscission can severely reduce crop quality and yield.
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