Biomedical and Health Sciences

This dataset contains RF (Radio Frequency) signals obtained from simulations, which model ultrasound propagation in cortical bone.

The simulations were designed to provide insights into the behaviour of ultrasound waves in cortical bone tissues, both in intact and pathological conditions. The dataset covers a wide range of parameters, including varying thickness (1-8 mm), porosity (1-20%), and frequency (1-8 MHz), allowing to explore the impact of these factors on ultrasound signal characteristics.

 

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Problems of neurodegenerative disorder patients can be solved by developing Brain-Computer Interface (BCI) based solutions. This requires datasets relevant to the languages spoken by patients. For example, Marathi, a prominent language spoken by over 83 million people in India, lacks BCI datasets based on the language for research purposes. To tackle this gap, we have created a dataset comprising Electroencephalograph (EEG) signal samples of selected common Marathi words.

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Background: The endpoint of liposuction surgery in a current clinical dilemma. The liposuction volume (LV) should be individualized according to each patient's objective and subjective needs.

Objectives: To create a mathematical model to quantitatively predict the change ratio of thigh circumference to LV.

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This data set consists of 16 half-hour EEG recordings, obtained from 10 volunteers, as described below.

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Objective: As one branch of human-computerinteraction, affective Brain-Computer Interfaces (aBCI) interpretand utilize electroencephalogram (EEG) signalsto achieve real-time monitoring and recognition of individualemotional states, opening new possibilities foremotion-aware technologies and applications. However,the challenge of individual differences in EEG emotiondata severely constrains the effectiveness and generalizationcapability of existing models.

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MS-BioGraphs are a family of sequence similarity graph datasets with up to 2.5 trillion edges. The graphs are weighted edges and presented in compressed WebGraph format. The dataset include symmetric and asymmetric graphs. The largest graph has been created by matching sequences in Metaclust dataset with 1.7 billion sequences. These real-world graph dataset are useful for measuring contributions in High-Performance Computing and High-Performance Graph Processing.

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459 Views

Healthcare 4.0 introduces the groundbreaking notion of the "Digital Twin (DT)," utilizing a digital model to encompass an individual's biological traits. This technology allows for the development of tailored treatment approaches, supports timely interventions, monitors respiratory issues, and offers decision-making assistance to healthcare professionals, ultimately advancing healthcare capabilities.

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We investigated the effect of CW radiation at 0.6 THz on pathological protein aggregates in the form of amyloid fibrils, i.e. ordered protein complexes linked to neurodegenerative diseases such as Parkinson's and Frontotemporal Dementia. To monitor the effect of THz irradiation we exploited mid-infrared (mid-IR) vibrational spectroscopy in the amide-I band range, whose lineshape is known to depend on the protein conformation and on how proteins arrange into ordered supramolecular complexes such as fibrils.

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The rapid development of highly multiplexed microscopy systems has enabled the study of cells embedded within their native tissue. The rich spatial data provided by these techniques have yielded exciting insights into the spatial features of human disease. However, computational methods for analyzing these high-content images are still emerging, and there is a need for more robust and generalizable tools for evaluating the cellular constituents and underlying stroma captured by high-plex imaging.

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A dataset comprising a total of 21 individuals has been meticulously compiled, with 9 individuals identified as exhibiting Major Depressive Disorder (MDD) based on the outcomes derived from the PHQ-9 Questionnaire. The remaining 12 individuals in the dataset are classified as non-MDD. 

The dataset encompasses diverse sensor data, including temperature measurements, SpO2 readings, pulse rates, and accelerometer data. It is important to note that all data points were collected within a controlled environment, ensuring reliability and consistency throughout the dataset.

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