A new 3D graphical representation of DNA sequences is introduced. This representation is called 3D-dynamic representation. It is a generalization of the 2D-dynamic dynamic representation. The sequences are represented by sets of "material points" in the 3D space. The resulting 3D-dynamic graphs are treated as rigid bodies. The descriptors characterizing the graphs are analogous to the ones used in the classical dynamics. The classification diagrams derived from this representation are presented and discussed. Due to the third dimension, "the history of the graph" can be recognized graphically because the 3D-dynamic graph does not overlap with itself. Specific parts of the graphs correspond to specific parts of the sequence. This feature is essential for graphical comparisons of the sequences. Numerically, both 2D and 3D approaches are of high quality. In particular, a difference in a single base between two sequences can be identified and correctly described (one can identify which base) by both 2D and 3D methods.
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http://dx.doi.org/10.1007/s00894-014-2141-8 | DOI Listing |
Comput Methods Programs Biomed
July 2023
Aix Marseille Univ, Universite de Toulon, CNRS, LIS, Marseille, France. Electronic address:
Background And Objective: Pelvic floor disorders are prevalent diseases and patient care remains difficult as the dynamics of the pelvic floor remains poorly understood. So far, only 2D dynamic observations of straining exercises at excretion are available in the clinics and 3D mechanical defects of pelvic organs are not well studied. In this context, we propose a complete methodology for the 3D representation of non-reversible bladder deformations during exercises, combined with a 3D representation of the location of the highest strain areas on the organ surface.
View Article and Find Full Text PDFJ Prosthet Dent
July 2024
Founder and Director Kois Center, Seattle, Wash; Affiliate Professor, Graduate Prosthodontics, Department of Restorative Dentistry, University of Washington, Seattle, Wash; Private practice, Seattle, Wash.
A technique for digitally recording the maxillomandibular relationship, including the maximum intercuspation and centric occlusion and the patient's mandibular motion, by using an optical jaw tracking system is described. Advantages of this technique include the digital registration of the maxillomandibular relationship and mandibular motion. This technique incorporates the mandibular motion into the 3-dimensional (3D) virtual patient representation to integrate the 3D dynamic virtual patient visualization.
View Article and Find Full Text PDFBMC Med Imaging
May 2022
School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, 310018, People's Republic of China.
Purpose: Compressed Sensing Magnetic Resonance Imaging (CS-MRI) is a promising technique to accelerate dynamic cardiac MR imaging (DCMRI). For DCMRI, the CS-MRI usually exploits image signal sparsity and low-rank property to reconstruct dynamic images from the undersampled k-space data. In this paper, a novel CS algorithm is investigated to improve dynamic cardiac MR image reconstruction quality under the condition of minimizing the k-space recording.
View Article and Find Full Text PDFComb Chem High Throughput Screen
January 2022
Department of Radiological Informatics and Statistics, Medical University of Gdańsk, 80-210 Gdańsk,Poland.
Unlabelled: The aim of the studies is to show that graphical bioinformatics methods are good tools for the description of genome sequences of viruses. A new approach to the identification of unknown virus strains, is proposed.
Methods: Biological sequences have been represented graphically through 2D and 3D-Dynamic Representations of DNA/RNA Sequences - theoretical methods for the graphical representation of the sequences developed by us previously.
3D dynamic point clouds provide a natural discrete representation of real-world objects or scenes in motion, with a wide range of applications in immersive telepresence, autonomous driving, surveillance, etc. Nevertheless, dynamic point clouds are often perturbed by noise due to hardware, software or other causes. While a plethora of methods have been proposed for static point cloud denoising, few efforts are made for the denoising of dynamic point clouds, which is quite challenging due to the irregular sampling patterns both spatially and temporally.
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