Biomedical and Health Sciences
The data included here within is the associated model training results from the correlated paper "Distribution-Driven Augmentation of Real-World Datasets for Improved Cancer Diagnostics With Machine Learning". This paper focuses on using kernel density estimators to curate datasets by balancing classes and filling missing null values though synthetically generated data. Additionally, this manuscript proposes a technique for joining distinct datasets to train a model with necessary features from multiple different datasets as a type of transfer-learning.
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Cemented total hip arthroplasty (THA) demonstrates superior survival rates compared to uncemented procedures. Nevertheless, most younger patients opt for uncemented THA, as removing well-fixed bone cement in the femur during revisions is complex, particularly the distal cement plug. This removal procedure often increases the risk of femoral fracture or perforation, haemorrhage and weakening bone due to poor drill control and positioning. Aim of this study was to design a novel drill guide to improve drill positioning.
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This dataset protocol details the acquisition of a surface electromyography (sEMG) dataset from the Tibialis Anterior (TA) and Gastrocnemius Lateralis (GL) muscles of 20 healthy adults, with an equal distribution of 10 male and 10 female participants. The data was collected using the Delsys Trigno wireless EMG system during a 30-second walking session. Proper electrode placement on the specified muscles was ensured for accurate signal capture. Ethical considerations were addressed, with approval from the Institutional Review Board and informed consent from participants.
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Raw data of 'Tetherless Multi-targeted Bioimpedance Device for Monitoring Peripheral Artery Disease Progression'
The dataset includes results from measurements of pulse signals from three different arteries using a commercial bioimpedance device mentioned in the paper, both using the conventional method and the proposed approach.
Moreover, the dataset encompasses simulated outcomes derived from HFSS (High-Frequency Structure Simulator), specifically investigating the impact of different plaque sizes on current induction at various frequencies.
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Endoscopic images of patients with AIG, type B gastritis, and CNAG were collected between January 1st, 2019 and March 1st, 2023. All endoscopic images were acquired by Olympus Evis Lucera 260/290 (Tokyo, Japan) and FUJIFILM EC-760RV/M (Tokyo, Japan). This dataset includes 3576 endoscopic images. Poor image quality, images of the pharynx, images of the esophageal region, and images of the duodenal region were excluded.
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During our research in generating or optimizing molecules to be drug candidates by extending deep reinforcement learning and graph neural networks algorithms, we used GEOM data [1], and we had an idea to make a dataset obtained from molecules from GEOM to predit the activity towards COVID and the drug linkeness. We calculated over 200 descriptors for the molecules using RDKit [2]. We hope you enjoy using it.
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The pathology files of 194 colon cancer patients, 137 breast cancer patients, 124 gastric cancer patients, and 169 thyroid cancer patients who were referred to the healthcare facilities of Qazvin Province, Iran were examined for age, sex, surgery type, and pathological information. We collected information between 2010 and 2020.
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Blood pressure (BP) measurement is an indispensable parameter for diagnosing many diseases, e.g., heart attack, stroke, vascular disease, and kidney disease. All these disease sometimes lead to fatal injuries due to the failure of vital human organs. The measurement of BP using BP device has several inaccuracies due to the non-availability of SI traceable calibration systems, which can also meet the criteria of International Organization of Legal Metrology (OIML) particularly OIML R 148 and OIML R 149 guidelines.
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EEG Data Acquisition Using the Muse Device: Meditation and Rest Stages of Participants
This study aimed to investigate electroencephalogram (EEG) patterns during meditation to gain insights into the distinctions among practitioners with differing levels of experience. We analyzed EEG data from a cohort of seven participants with a unique background in Vipassana meditation, in order to discern how disparities in experience levels manifest in their neural activity.
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The Numerical Latin Letters (DNLL) dataset consists of Latin numeric letters organized into 26 distinct letter classes, corresponding to the Latin alphabet. Each class within this dataset encompasses multiple letter forms, resulting in a diverse and extensive collection. These letters vary in color, size, writing style, thickness, background, orientation, luminosity, and other attributes, making the dataset highly comprehensive and rich.
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