Rodent models are increasingly used to study refractive eye development and development of refractive errors; however, there is still some uncertainty regarding the accuracy of the optical models of the rat and mouse eye primarily due to high variability in reported ocular parameters. In this work, we have systematically evaluated the contribution of various ocular parameters, such as radii of curvature of ocular surfaces, thicknesses of ocular components, and refractive indices of ocular refractive media, using variational analysis and a computational model of the rodent eye. Variational analysis revealed that not all variation in ocular parameters has critical impact on the refractive status of the eye. Variation in the depth of the vitreous chamber, thickness of the lens, radius of the anterior surface of the cornea, radius of the anterior surface of the lens, as well as refractive indices for the lens and vitreous, appears to have the largest impact on the refractive error. The radii of the posterior surfaces of the cornea and lens have much smaller contributions to the refractive state. These data provide the framework for further refinement of the optical models of the rat and mouse eye and suggest that extra efforts should be directed towards increasing the linear resolution of the rodent eye biometry and obtaining more accurate data for the refractive indices of the lens and vitreous.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3829552 | PMC |
| http://dx.doi.org/10.1364/BOE.4.002585 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
BIOTIC: a Bayesian framework to integrate single-cell multi-omics for transcription factor activity inference and improve identity characterization of cells.
Brief Bioinform
November 2024
Department of Automation, Xiamen University, Xiang'an South Road, Xiang'an, 361102, Xiamen, Fujian, China.
Understanding cell destiny requires unraveling the intricate mechanism of gene regulation, where transcription factors (TFs) play a pivotal role. However, the actual contribution of TFs, that is TF activity, is not only determined by TF expression, but also accessibility of corresponding chromatin regions. Therefore, we introduce BIOTIC, an advanced Bayesian model with a well-established gene regulation structure that harnesses the power of single-cell multi-omics data to model the gene expression process under the control of regulatory elements, thereby defining the regulatory activity of TFs with variational inference.
View Article and Find Full Text PDFIntegrating single-cell multimodal epigenomic data using 1D convolutional neural networks.
Bioinformatics
December 2024
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, United States.
Motivation: Recent experimental developments enable single-cell multimodal epigenomic profiling, which measures multiple histone modifications and chromatin accessibility within the same cell. Such parallel measurements provide exciting new opportunities to investigate how epigenomic modalities vary together across cell types and states. A pivotal step in using these types of data is integrating the epigenomic modalities to learn a unified representation of each cell, but existing approaches are not designed to model the unique nature of this data type.
View Article and Find Full Text PDFECG signal generation using feature disentanglement auto-encoder.
Physiol Meas
January 2025
Harbin Institute of Technology, Harbin Institute of Technology, Harbin, 150001, CHINA.
Objective: The demand for ECG datasets, particularly those containing rare classes, poses a significant challenge as deep learning becomes increasingly prevalent in ECG signal research. While Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are widely adopted, they encounter difficulties in effectively generating samples for classes with limited instances.
Approach: To address this issue, we propose a novel Feature Disentanglement Auto-Encoder (FDAE) designed to dissect various generative factors under a contrastive learning framework within ECG data to facilitate the generation of new ECG samples.
Variational graph autoencoder for reconstructed transcriptomic data associated with NLRP3 mediated pyroptosis in periodontitis.
Sci Rep
January 2025
Department of Basic Sciences, Faculty of Dentistry, Universidad de Antioquia U de A, Medellín, 050010, Colombia.
The NLRP3 inflammasome, regulated by TLR4, plays a pivotal role in periodontitis by mediating inflammatory cytokine release and bone loss induced by Porphyromonas gingivalis. Periodontal disease creates a hypoxic environment, favoring anaerobic bacteria survival and exacerbating inflammation. The NLRP3 inflammasome triggers pyroptosis, a programmed cell death that amplifies inflammation and tissue damage.
View Article and Find Full Text PDFBackground And Aims: The importance of risk stratification in patients with chest pain extends beyond diagnosis and immediate treatment. This study sought to evaluate the prognostic value of electrocardiogram feature-based machine learning models to risk-stratify all-cause mortality in those with chest pain.
Methods: This was a prospective observational cohort study of consecutive, non-traumatic patients with chest pain.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!