Biomedical and Health Sciences

This paper presents data on the life of 1000 Bedlington Terriers. Observations were conducted from 2015 to 2021. The oldest pet in 2021 was 17 years old, the youngest was 6.

The table shows the correlation between the life expectancy of Bedlington Terriers with copper toxicosis and healthy ones.

On average, the life expectancy of sick dogs is reduced by 44%, however, about 12% of dogs are susceptible to the disease. 40% of sick dogs die within 3 years, the rest within 5 years, extremely rare are individuals living more than 5 years.

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This dataset comprises extensive multi-modal data related to the experimental study of ultrasonically excited pulsating fluid jets used for bone cement removal. Conducted at the Institute of Geonics, Ostrava, Czech Republic, the study explores the effect of varying standoff distances on erosion profiles, under controlled parameters including a fixed nozzle diameter, sonotrode frequency, supply pressure, and robot arm velocity. The dataset includes numerical data representing ablation profiles, captured as a large CSV file, and audio recordings captured using a high-resolution microphone.

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Precise prediction of potential drug-disease associations (DDAs) is essential for enhancing treatment strategies and expediting drug development. However, current methods often rely on single-modal data and fail to effectively integrate multimodal information when representing node attributes.

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As various modalities of genomic data are accumulating, methods to integrate across multi-omics datasets are becoming important. Error-correcting output codes (ECOC) is an ensemble learning strategy for solving a multiclass problem thru a decoding process that aggregates the predictions of multiple classifiers. Thus, it lends itself naturally to aggregating predictions across multiple views as well. We applied the ECOC to multi-view learning to see if this strategy can enhance classifier performance as compared to traditional techniques.

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44 Views

In structure-based drug design (SBDD), a major challenge is generating high-affinity 3D ligand molecules that can effectively bind to specific protein targets, which requires accurately capturing complex protein-ligand interactions. Although existing diffusion models have demonstrated potential in molecular generation tasks, they often struggle with accurately capturing the complex interactions between proteins and ligands. To address this problem, we propose MSIDiff, a multi-stage interaction-aware diffusion model for protein-specific molecular generation.

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87 Views

Osteoarthritis (OA) is a prevalent degenerative joint disease,particularly affecting the knees. Early and accurate detection of OA and its severity, often graded using the Kellgren-Lawrence (KL) scale, is crucial for timely intervention and management. This study explores the application of deep learning techniques to automatically detect OA and assign KL grades from knee X-ray images. We propose a novel deep learning architecture that effectively extracts relevant features from X-ray images and classifies them into different KL grades.

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244 Views

This dataset comprises a comprehensive collection of PubMed abstracts and associated metadata focusing on the topic of multiple sclerosis (MS) in relation to social determinants and environmental factors, spanning publications from January 1, 2018, to December 31, 2023. The data was meticulously gathered using the PubMed E-Utilities API with the search query "multiple sclerosis" AND ("social determinants" OR "environmental factors"). Articles classified as preprints were excluded to ensure the inclusion of peer-reviewed research only.

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C-SRR Radiation Patterns. The pixels from the radiation pattern generated by positioning the C-SRR over the phantom model with and without cancerous tissue were extracted using various window shapes and sizes to form the dataset. For this, pixel sampling operations such as average, minimum, maximum, and median are performed. Pixel reconfiguration to triangle, square, symmetric and asymmetric is also performed. In average pixel sampling, the average value of the pixel color is divided by the total number of pixels.

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Abstract— Pluripotent cell types retain several characteristics that make them optimal cell source material for applications in drug development, disease modeling, and therapeutic applications. Human induced pluripotent stem cells (hiPSCs) are currently the most accessible cell source material to cultivate and derive cell-based therapeutic solutions at scale. However, a disconnect exists between quality characteristics of phenotype in the pluripotent state, and downstream metrics for efficacy.

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