Biomedical and Health Sciences

We prepared a new dataset (Cov-Pneum) for X-ray images by processing and merging three well-known publicly available datasets from Kaggle [1]–[4]. This dataset includes a total of 21,272 CXR images of COVID-19 (COVID-19 virus-infected lung), pneumonia (pneumonia-infected lung), and normal (clear lung) and consisting of images 4296, 5824, and 11152, respectively. We applied image scaling and preprocessing operations to enhance the quality of these CXR images.
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In early 2019, we developed a manually curated database named lncR2metasta to provide a comprehensive repository for the regulations of long non-coding RNAs (lncRNAs, an important ncRNA type) during various CMEs. We updated this database this year by supplementing other two important ncRNA types, microRNAs (miRNAs) and circular RNAs (circRNAs), for their involvement during various CMEs after a thorough manual curation from published studies.
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Simultaneously-collected multimodal Mannequin Lying pose (SMaL) dataset is a infant pose dataset based on a posable mannequin. The SMaL dataset contains a set of 300 unique poses under three cover conditions using three sensor modalities: color imaging, depth sensing, and pressure sensing. It represents the first multimodal dataset for infant pose estimation and the first dataset to explore under the cover pose estimation for infants.
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Electroretinography is a non-invasive electrophysiological method standardized by the International Society for Clinical Electrophysiology of Vision (ISCEV). Electroretinography has been used for the clinical application and standardization of electrophysiological protocols for diagnosing the retina since 1989. Electroretinography become fundamental ophthalmological research method that may assesses the state of the retina. To transfer clinical practice to patients the establishment of standardized protocols is an important step.
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The code contains two public Parkinson's speech datasets, a self-collected Parkinson's speech dataset, some common public datasets. It also contains the MATLAB code for Intra-subject Enveloped Deep Sample Fuzzy Ensemble Learning Algorithm of Speech Data of Parkinson's Disease( JTEHM-00114-2022).
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Hydrogel scaffolds have attracted attention to develop cellular therapy and tissue engineering platforms for regenerative medicine applications. Among factors, local mechanical properties of scaffolds drive the functionalities of cell niche. Dynamic mechanical analysis (DMA), the standard method to characterize mechanical properties of hydrogels, restricts development in tissue engineering because the measurement provides a single elasticity value for the sample, requires direct contact, and represents a destructive evaluation preventing longitudinal studies on the same sample.
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