Biomedical and Health Sciences
This is a videoconference between a witness about murders who is a victim of many crimes and a law firm. This witness is called Colin Paul Gloster. This law firm is called Pais do Amaral Advogados.
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With the development of artificial intelligence, new possibilities have been opened up for the diagnosis and prevention of osteoporosis. We have successfully constructed an osteoporosis risk prediction model using deep learning algorithms, combined with demographic data and laboratory results. To further promote human health, we will publicly disclose the dataset we used, which includes 2,186 cases of males over 50 years old and postmenopausal females.
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We present the SynSUM benchmark, a synthetic dataset linking unstructured clinical notes to structured background variables. The dataset consists of 10,000 artificial patient records containing tabular variables (like symptoms, diagnoses and underlying conditions) and associated clinical notes describing the fictional patient encounter in the domain of respiratory diseases. The tabular portion of the data is generated through a Bayesian network, where both the causal structure between the variables and the conditional probabilities are proposed by an expert based on domain knowledge.
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This dataset contains high-resolution retinal fundus images collected from 495 unique subjects from Eye Care hospital in Aizawl, Mizoram, for diabetic retinopathy (DR) detection and classification. The images were captured over five years using the OCT RS 330 device, which features a 45° field of view (33° for small-pupil imaging), a focal length of 45.7 mm, and a 6.25 mm sensor width. Each image was acquired at a resolution of 3000x3000 pixels, ensuring high diagnostic quality and the visibility of subtle features like microaneurysms, exudates, and hemorrhages.
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This dataset comprises a comprehensive analysis of state-of-the-art techniques and systems for seizure detection and classification, based on various papers and studies. It integrates detailed metadata on publications, including their year, methodologies, seizure types (both ILAE-2017 and paper-specific), datasets, and biomarker utilization. The dataset also provides performance metrics such as accuracy, sensitivity, specificity, false-positive rates, and AUC-ROC values, alongside additional technical details about machine learning models, feature extraction techniques, and biomarkers.
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We propose AcuGRL, a graph representation learning-based framework that models the relationships between acupoints and disease phenotypes as a heterogeneous graph. This framework incorporates a domain knowledge-guided scheme to capture both the structural and semantic features of the network, generating effective embeddings for downstream tasks. Additionally, we integrate micro-level genetic targets with macro-level disease phenotypes to further enhance network connectivity and provide richer contextual information.
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The proper evaluation of food freshness is critical to ensure safety, quality along with customer satisfaction in the food industry. While numerous datasets exists for individual food items,a unified and comprehensive dataset which encompass diversified food categories remained as a significant gap in research. This research presented UC-FCD, a novel dataset designed to address this gap.
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We developed a control framework for an ankle-powered prosthesis that is based on a neuromusculoskeletal model driven by muscle synergies, aimed at mimicking human motor control strategies during cyclic movements.Refer to recent publication for more details on the research goals and methodology [1]. The personalized muscle synergy model computes muscle excitations at each timestamp of every trial using sensory information: gait phase was calculated using real-time ground reaction forces information, speed information was measured from the instrumented treadmill.In this study, both the gait
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This dataset includes stress relaxation and performance test results conducted on a steerable catheter to evaluate and validate its viscoelastic properties using a quasilinear viscoelastic (QLV) beam model. Both datasets consist of raw bending moment measurements from a load cell and processed curvature data derived from stereo images.
The relaxation test data provides insights into viscoelastic behavior by illustrating the relationship between moment and curvature across multiple loading levels. This data is used to identify the parameters of the QLV beam model.
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