[This corrects the article DOI: 10.1371/journal.pcbi.1007862.].
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8714014 | PMC |
| http://dx.doi.org/10.1371/journal.pcbi.1009710 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
The Role of Morphological Structure in Determining the Optimal Viewing Position During Visual Word Recognition in Beginning Readers.
Children (Basel)
November 2024
Univ. Lille, CNRS, UMR 9193-SCALab-Sciences Cognitives et Sciences Affectives, F-59000 Lille, France.
Background/objectives: The present study examines the role of morphemic units in the initial word recognition stage among beginning readers. We assess whether and to what extent sublexical units, such as morphemes, are used in processing French words and how their use varies with reading proficiency.
Methods: Two experiments were conducted to investigate the perceptual and morphological effects on the recognition of words presented in central vision, using a variable-viewing-position technique.
State-of-the-Art Trends in Data Compression: COMPROMISE Case Study.
Entropy (Basel)
November 2024
Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, SI-2000 Maribor, Slovenia.
After a boom that coincided with the advent of the internet, digital cameras, digital video and audio storage and playback devices, the research on data compression has rested on its laurels for a quarter of a century. Domain-dependent lossy algorithms of the time, such as JPEG, AVC, MP3 and others, achieved remarkable compression ratios and encoding and decoding speeds with acceptable data quality, which has kept them in common use to this day. However, recent computing paradigms such as cloud computing, edge computing, the Internet of Things (IoT), and digital preservation have gradually posed new challenges, and, as a consequence, development trends in data compression are focusing on concepts that were not previously in the spotlight.
View Article and Find Full Text PDFSTMGraph: spatial-context-aware of transcriptomes via a dual-remasked dynamic graph attention model.
Brief Bioinform
November 2024
Center for Genomics and Biotechnology, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Haixia Institute of Science and Technology, Fujian Agriculture and Forestry University, No. 15 Shangxiadian Road, Cangshan District, Fuzhou 350002, China.
Spatial transcriptomics (ST) technologies enable dissecting the tissue architecture in spatial context. To perceive the global contextual information of gene expression patterns in tissue, the spatial dependence of cells must be fully considered by integrating both local and non-local features by means of spatial-context-aware. However, the current ST integration algorithm ignores for ST dropouts, which impedes the spatial-aware of ST features, resulting in challenges in the accuracy and robustness of microenvironmental heterogeneity detecting, spatial domain clustering, and batch-effects correction.
View Article and Find Full Text PDFUnlabelled: Cytoplasmic proteins must recruit to membranes to function in processes such as endocytosis and cell division. Many of these proteins recognize not only the chemical structure of the membrane lipids, but the curvature of the surface, binding more strongly to more highly curved surfaces, or 'curvature sensing'. Curvature sensing by amphipathic helices is known to vary with membrane bending rigidity, but changes to lipid composition can simultaneously alter membrane thickness, spontaneous curvature, and leaflet symmetry, thus far preventing a systematic characterization of lipid composition on such curvature sensing through either experiment or simulation.
View Article and Find Full Text PDFEnhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images.
Curr Med Imaging
January 2025
Department of Electrical Engineering, Government College University, Lahore, Pakistan.
Background: Colon diseases are major global health issues that often require early detection and correct diagnosis to be effectively treated. Deep learning approaches and recent developments in medical imaging have demonstrated promise in increasing diagnostic accuracy.
Objective: This work suggests that a Convolutional Neural Network (CNN) model paired with other models can detect different gastrointestinal (GI) abnormalities or diseases from endoscopic images via the fusion of residual blocks, including alpha dropouts (αDO) and auxiliary fusing layers.
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!