Internal fixator is usually adopted in the treatment of bone fractures. In order to achieve anatomical reduction and effective fixation of fractures, the placement of internal fixators should comply with the biology force line of the bone and adapt to the specific anatomical morphological characteristics of the cortical bone. In order to investigate the distribution characteristics and formation regularity of biology force line and cortical thickness of human bone, three-dimensional model of proximal femur is established by using three-dimensional reconstruction technique in this paper. The normal physiological stress distribution of proximal femur is obtained by finite element analysis under three kinds of behavior conditions: one-legged stance, abduction and adduction. The structural topology optimization method is applied to simulate the cortex of the proximal femur under the combined action of three kinds of behavior conditions, and the anatomic morphological characteristics of the proximal femur are compared. The distribution trend of biology force line of proximal femur and the characteristics of cortex are analyzed. The results show that the biology force lines of bone structure and the morphological characteristics of cortex depend on the load of human activities. The distribution trend of biology force line is related to the direction of trabecular bone and the ridge trend and firmness of cortex when bone is loaded physiologically. The proposed analytical method provides a solution to determine the biology force line of bone and the distribution characteristics of cortex. The conclusions obtained may guide the reasonable placement of internal fixator components of fracture.
Objective To investigate the differences in the topology of functional brain networks between populations with good spatial navigation ability and those with poor spatial navigation ability. Methods From September 2020 to September 2021, 100 college students from PLA Army Border and Coastal Defense Academy were selected to test the spatial navigation ability. The 25 students with the highest spatial navigation ability were selected as the GN group, and the 25 with the lowest spatial navigation ability were selected as the PN group, and their resting-state functional MRI and 3D T1-weighted structural image data of the brain were collected. Graph theory analysis was applied to study the topology of the brain network, including global and local topological properties. Results The variations in the clustering coefficient, characteristic path length, and local efficiency between the GN and PN groups were not statistically significant within the threshold range (P>0.05). The brain functional connectivity networks of the GN and PN groups met the standardized clustering coefficient (γ)>1, the standardized characteristic path length (λ)≈1, and the small-world property (σ)>1, being consistent with small-world network property. The areas under curve (AUCs) for global efficiency (0.22±0.01 vs. 0.21±0.01), γ value (0.97±0.18 vs. 0.81±0.18) and σ value (0.75±0.13 vs. 0.64±0.13) of the GN group were higher than those of the PN group, and the differences were statistically significant (P<0.05); the between-group difference in AUC for λ value was not statistically significant (P>0.05). The results of the nodal level analysis showed that the AUCs for nodal clustering coefficients in the left superior frontal gyrus of orbital region (0.29±0.05 vs. 0.23±0.07), the right rectus gyrus (0.29±0.05 vs. 0.23±0.09), the middle left cingulate gyrus and its lateral surround (0.22±0.02 vs. 0.25±0.02), the left inferior occipital gyrus (0.32±0.05 vs. 0.35±0.05), the right cerebellar area 3 (0.24±0.04 vs. 0.26±0.03), and the right cerebellar area 9 (0.22±0.09 vs. 0.13±0.13) were statistically different between the two groups (P<0.05). The differences in AUCs for degree centrality and nodal efficiency between the two groups were not statistically significant (P>0.05). Conclusions Compared with people with good spatial navigation ability, the topological properties of the brains of the ones with poor spatial navigation ability still conformed to the small-world network properties, but the connectivity between brain regions reduces compared with the good spatial navigation ability group, with a tendency to convert to random networks and a reduced or increased nodal clustering coefficient in some brain regions. Differences in functional brain network connectivity exist among people with different spatial navigation abilities.
In view of the evaluation of fundus image segmentation, a new evaluation method was proposed to make up insufficiency of the traditional evaluation method which only considers the overlap of pixels and neglects topology structure of the retinal vessel. Mathematical morphology and thinning algorithm were used to obtain the retinal vascular topology structure. Then three features of retinal vessel, including mutual information, correlation coefficient and ratio of nodes, were calculated. The features of the thinned images taken as topology structure of blood vessel were used to evaluate retinal image segmentation. The manually-labeled images and their eroded ones of STARE database were used in the experiment. The result showed that these features, including mutual information, correlation coefficient and ratio of nodes, could be used to evaluate the segmentation quality of retinal vessel on fundus image through topology structure, and the algorithm was simple. The method is of significance to the supplement of traditional segmentation evaluation of retinal vessel on fundus image.
Sleep disorders and sleep disturbances are gaining increasing attention, and accurate sleep staging is key to assessing sleep quality. Existing studies utilize spatio-temporal features of multi-channel physiological signals such as EEG and spatial topological information of brain regions for sleep staging, but they suffer from insufficient mining of spatio-temporal correlations, neglect of the nonlinear geometric structure of signals, and feature redundancy, leading to low accuracy in sleep stage classification. To address these problems, this paper proposes a multi-view spatio-temporal convolutional integration network based on manifold learning. Firstly, three types of feature representations of multi-physiological signals are constructed via short-time Fourier transform, discrete wavelet transform, and raw signal processing. Secondly, an attention module that fuses Euclidean space and manifold geometry is designed to represent and enhance multi-modal physiological signals on the Riemannian symmetric positive definite manifold, mining their potential spatio-temporal representations. Finally, combining these three types of sleep features from adjacent time segments, long short-term memory (LSTM) is used to model temporal dependencies to accomplish sleep stage classification. Experimental results on public sleep stage classification datasets show that the proposed method outperforms existing mainstream methods in accuracy, F1-score, and Kappa coefficient, and ablation studies validate the effectiveness of the manifold attention mechanism and multi-view feature combination. This work provides a new method for nonlinear fusion of multi-modal physiological signals.