An approach, computational shape analysis (CSA), is presented here which utilizes a Fourier-wavelet representation to numerically describe shape features of biological forms. Two elements are involved: 1) elliptical Fourier functions (EFFs), to provide estimates of global aspects, and 2) continuous wavelet transforms (CWTs) to generate an objective estimate of localized features. EFFs are computed, using a set of pseudohomologous points, to create a precise analog of the boundary. This computed contour is then normalized by scaling and rotated in two-dimensional space to insure a representation that is invariant with respect to starting point, size, and orientation. The predicted point coordinates derived from the EFFs are submitted to CWT for further processing. Wavelet coefficients are then computed to identify localized features, localization being a subjective process with EFFs. The advantage of wavelets is that they eliminate the inevitable subjectivity inherent in the choice of measurements. To test the usefulness of CSA, a sample of cranial base (CB) lateral radiographic outlines was available. Five archaeological periods, Yayoi, Kofun, Kamakura, Edo, and Modern, were utilized (n = 297). Statistically significant differences in sex and archaeological age were found. Although archaeological age differences were present, they were small and random in character, suggesting stability in the CB structures. In contrast, sexually dimorphic differences were present in every group from the Yayoi to the Modern period. This presence of sexually dimorphic differences in shape was consistent with earlier studies of M. nemestrina, G. gorilla, and P. troglodytes. Consequently, it is suggested that the pattern of sexual dimorphism documented in the Japanese CB is a primate pattern with an ancient evolutionary history. The results demonstrate, both visually and numerically, that CSA is a powerful approach for describing both global and localized features of craniofacial structures such as the CB.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1002/ajpa.20209 | DOI Listing |
Clin Epigenetics
January 2025
Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
Alcohol consumption is an important risk factor for multiple diseases. It is typically assessed via self-report, which is open to measurement error through recall bias. Instead, molecular data such as blood-based DNA methylation (DNAm) could be used to derive a more objective measure of alcohol consumption by incorporating information from cytosine-phosphate-guanine (CpG) sites known to be linked to the trait.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Biomedical Engineering, School of Life Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China.
The cervical cell classification technique can determine the degree of cellular abnormality and pathological condition, which can help doctors to detect the risk of cervical cancer at an early stage and improve the cure and survival rates of cervical cancer patients. Addressing the issue of low accuracy in cervical cell classification, a deep convolutional neural network A2SDNet121 is proposed. A2SDNet121 takes DenseNet121 as the backbone network.
View Article and Find Full Text PDFSci Rep
January 2025
School of Food and Pharmacy, Zhejiang Ocean University, Zhoushan, 316022, People's Republic of China.
Accurate and rapid segmentation of key parts of frozen tuna, along with precise pose estimation, is crucial for automated processing. However, challenges such as size differences and indistinct features of tuna parts, as well as the complexity of determining fish poses in multi-fish scenarios, hinder this process. To address these issues, this paper introduces TunaVision, a vision model based on YOLOv8 designed for automated tuna processing.
View Article and Find Full Text PDFSci Rep
January 2025
Ministry of Higher Education, Mataria Technical College, Cairo, 11718, Egypt.
The current work introduces the hybrid ensemble framework for the detection and segmentation of colorectal cancer. This framework will incorporate both supervised classification and unsupervised clustering methods to present more understandable and accurate diagnostic results. The method entails several steps with CNN models: ADa-22 and AD-22, transformer networks, and an SVM classifier, all inbuilt.
View Article and Find Full Text PDFNat Commun
January 2025
Center for High Pressure Science, State Key Laboratory of Metastable Materials Science and Technology, Yanshan University, Qinhuangdao, 066004, China.
Hydrous aluminosilicates are important deep water-carriers in sediments subducting into the deep mantle. To date, it remains enigmatic how hydrous aluminosilicates withstand extremely high temperatures in the mantle transition zone. Here we systematically investigate the crystal structures and chemical compositions of typical hydrous aluminosilicates using single-crystal X-ray diffraction, electron probe microanalyzer, and nanoscale secondary ion mass spectrometry.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!