Morphological studies of insects can help us to understand the concomitant or sequential functionality of complex structures and may be used to hypothetize distinct levels of phylogenetic relationship among groups. Traditional morphological works, generally, have encompassed a set of elements, including descriptions of structures and their respective conditions, literature references and images, all combined in a single document. Fast forward to the digital era, it is now possible to release this information simultaneously but also independently as data sets linked to the original publication in an external environment. In order to link data from various fields of knowledge, disseminating morphological information in an open environment, it is important to use tools that enhance interoperability. For example, semantic annotations facilitate the dissemination and retrieval of phenotypic data in digital environments. The integration of semantic (i.e. web-based) components with anatomic treatments can be used to generate a traditional description in natural language along with a set of semantic annotations. The ant genus Strumigenys currently comprises about 840 described species distributed worldwide. In the Neotropical region, almost 200 species are currently known, but it is possible that much of the species' diversity there remains unexplored and undescribed. The morphological diversity in the genus is high, reflecting an extreme generic reclassification that occurred in the late 20th and early 21st centuries. Here we define the anatomical concepts in this highly diverse group of ants using semantic annotations to enrich the anatomical ontologies available online, focussing on the definition of terms through subjacent conceptualization.
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http://dx.doi.org/10.1016/j.asd.2019.100877 | DOI Listing |
Sci Rep
December 2024
School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao, 266520, China.
This paper presents a deep learning model based on an active learning strategy. The model achieves accurate identification of vegetation types in the study area by utilizing multispectral data obtained from preprocessing of unmanned aerial vehicle (UAV) remote sensing equipment. This approach offers advantages such as high data accuracy, mobility, and easy data collection.
View Article and Find Full Text PDFJ Pathol Inform
January 2025
Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States of America.
With the increasing utilization of exome and genome sequencing in clinical and research genetics, accurate and automated extraction of human phenotype ontology (HPO) terms from clinical texts has become imperative. Traditional methods for HPO term extraction, such as PhenoTagger, often face limitations in coverage and precision. In this study, we propose a novel approach that leverages large language models (LLMs) to generate synthetic sentences with clinical context, which were semantically encoded into vector embeddings.
View Article and Find Full Text PDFProc (IEEE Int Conf Healthc Inform)
June 2024
National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
In radiology, Artificial Intelligence (AI) has significantly advanced report generation, but automatic evaluation of these AI-produced reports remains challenging. Current metrics, such as Conventional Natural Language Generation (NLG) and Clinical Efficacy (CE), often fall short in capturing the semantic intricacies of clinical contexts or overemphasize clinical details, undermining report clarity. To overcome these issues, our proposed method synergizes the expertise of professional radiologists with Large Language Models (LLMs), like GPT-3.
View Article and Find Full Text PDFJ Clin Med
November 2024
Department of Neurosurgery, College of Medicine, The University of Tennessee Health Sciences, Memphis, TN 38163, USA.
Lumbar spinal stenosis (LSS) is a major cause of chronic lower back and leg pain, and is traditionally diagnosed through labor-intensive analysis of magnetic resonance imaging (MRI) scans by radiologists. This study aims to streamline the diagnostic process by developing an automated radiology report generation (ARRG) system using a vision-language (VL) model. We utilized a Generative Image-to-Text (GIT) model, originally designed for visual question answering (VQA) and image captioning.
View Article and Find Full Text PDFGraefes Arch Clin Exp Ophthalmol
December 2024
School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Background: This work tackles the growing problem of early identification of diabetic retinopathy and diabetic macular edema. The deep neural network design utilizes multi-scale feature fusion to improve automated diagnostic accuracy. Methods This approach uses convolutional neural networks (CNN) and is designed to combine higher-level semantic inputs with low-level textural characteristics.
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