Design of a Robust Synchronization-Based Topology Observer for Complex Delayed Networks with Fixed and Adaptive Coupling Strength.

Network topology plays a key role in determining the characteristics and dynamical behaviors of a network. But in practice, network topology is sometimes hidden or uncertain ahead of time because of network complexity. In this paper, a robust-synchronization-based topology observer (STO) is proposed and applied to solve the problem of identifying the topology of complex delayed networks (TICDNs). In comparison to the existing literature, the proposed STO does not require any prior knowledge about the range of topological parameters and does not have strict limits on topology type. Furthermore, the proposed STO is suitable not only for networks with fixed coupling strength, but also for networks with adaptive coupling strength. Finally, a few comparison examples for TICDNs are used to verify the feasibility and efficiency of the proposed STO, and the results show that the proposed STO outperforms the other methods.

[Visual field prediction based on temporal-spatial feature learning].

Glaucoma stands as the leading irreversible cause of blindness worldwide. Regular visual field examinations play a crucial role in both diagnosing and treating glaucoma. Predicting future visual field changes can assist clinicians in making timely interventions to manage the progression of this disease. To integrate temporal and spatial features from past visual field examination results and enhance visual field prediction, a convolutional long short-term memory (ConvLSTM) network was employed to construct a predictive model. The predictive performance of the ConvLSTM model was validated and compared with other methods using a dataset of perimetry tests from the Humphrey field analyzer at the University of Washington (UWHVF). Compared to traditional methods, the ConvLSTM model demonstrated higher prediction accuracy. Additionally, the relationship between visual field series length and prediction performance was investigated. In predicting the visual field using the previous three visual field results of past 1.5~6.0 years, it was found that the ConvLSTM model performed better, achieving a mean absolute error of 2.255 dB, a root mean squared error of 3.457 dB, and a coefficient of determination of 0.960. The experimental results show that the proposed method effectively utilizes existing visual field examination results to achieve more accurate visual field prediction for the next 0.5~2.0 years. This approach holds promise in assisting clinicians in diagnosing and treating visual field progression in glaucoma patients.

Adaptive Predefined-Time Sliding Mode Control for QUADROTOR Formation with Obstacle and Inter-Quadrotor Avoidance.

In this paper, aiming at the problem of control and obstacle avoidance in quadrotor formation when mathematical modeling is not accurate, the artificial potential field method with virtual force is used to plan the obstacle avoidance path of quadrotor formation to solve the problem that the artificial potential field method may fall into local optimal. The adaptive predefined-time sliding mode control algorithm based on RBF neural networks enables the quadrotor formation to track the planned trajectory in a predetermined time and also adaptively estimates the unknown interference in the mathematical model of the quadrotor to improve the control performance. Through theoretical derivation and simulation experiments, this study verified that the proposed algorithm can make the planned trajectory of the quadrotor formation avoid obstacles and make the error between the true trajectory and the planned trajectory converge within a predetermined time under the premise of adaptive estimation of unknown interference in the quadrotor model.

Remote Blood Pressure Estimation via the Spatiotemporal Mapping of Facial Videos.

Blood pressure (BP) monitoring is vital in daily healthcare, especially for cardiovascular diseases. However, BP values are mainly acquired through a contact-sensing method, which is inconvenient and unfriendly for BP monitoring. This paper proposes an efficient end-to-end network for estimating BP values from a facial video to achieve remote BP estimation in daily life. The network first derives a spatiotemporal map of a facial video. Then, it regresses the BP ranges with a designed blood pressure classifier and simultaneously calculates the specific value with a blood pressure calculator in each BP range based on the spatiotemporal map. In addition, an innovative oversampling training strategy was developed to handle the problem of unbalanced data distribution. Finally, we trained the proposed blood pressure estimation network on a private dataset, MPM-BP, and tested it on a popular public dataset, MMSE-HR. As a result, the proposed network achieved a mean absolute error (MAE) and root mean square error (RMSE) of 12.35 mmHg and 16.55 mmHg on systolic BP estimations, and those for diastolic BP were 9.54 mmHg and 12.22 mmHg, which were better than the values obtained in recent works. It can be concluded that the proposed method has excellent potential for camera-based BP monitoring in the indoor scenarios in the real world.

FreeGaze: A Framework for 3D Gaze Estimation Using Appearance Cues from a Facial Video.

Gaze is a significant behavioral characteristic that can be used to reflect a person's attention. In recent years, there has been a growing interest in estimating gaze from facial videos. However, gaze estimation remains a challenging problem due to variations in appearance and head poses. To address this, a framework for 3D gaze estimation using appearance cues is developed in this study. The framework begins with an end-to-end approach to detect facial landmarks. Subsequently, we employ a normalization method and improve the normalization method using orthogonal matrices and conduct comparative experiments to prove that the improved normalization method has a higher accuracy and a lower computational time in gaze estimation. Finally, we introduce a dual-branch convolutional neural network, named FG-Net, which processes the normalized images and extracts eye and face features through two branches. The extracted multi-features are then integrated and input into a fully connected layer to estimate the 3D gaze vectors. To evaluate the performance of our approach, we conduct ten-fold cross-validation experiments on two public datasets, namely MPIIGaze and EyeDiap, achieving remarkable accuracies of 3.11° and 2.75°, respectively. The results demonstrate the high effectiveness of our proposed framework, showcasing its state-of-the-art performance in 3D gaze estimation.

The MAX2-KAI2 module promotes salicylic acid-mediated immune responses in Arabidopsis.

Arabidopsis MORE AXILLARY GROWTH2 (MAX2) is a key component in the strigolactone (SL) and karrikin (KAR) signaling pathways and regulates the degradation of SUPPRESSOR OF MAX2 1/SMAX1-like (SMAX1/SMXL) proteins, which are transcriptional co-repressors that regulate plant architecture, as well as abiotic and biotic stress responses. The max2 mutation reduces resistance against Pseudomonas syringae pv. tomato (Pst). To uncover the mechanism of MAX2-mediated resistance, we evaluated the resistance of various SL and KAR signaling pathway mutants. The resistance of SL-deficient mutants and of dwarf 14 (d14) was similar to that of the wild-type, whereas the resistance of the karrikin insensitive 2 (kai2) mutant was compromised, demonstrating that the KAR signaling pathway, not the SL signaling pathway, positively regulates the immune response. We measured the resistance of smax1 and smxl mutants, as well as the double, triple, and quadruple mutants with max2, which revealed that both the smax1 mutant and smxl6/7/8 triple mutant rescue the low resistance phenotype of max2 and that SMAX1 accumulation diminishes resistance. The susceptibility of smax1D, containing a degradation-insensitive form of SMAX1, further confirmed the SMAX1 function in the resistance. The relationship between the accumulation of SMAX1/SMXLs and disease resistance suggested that the inhibitory activity of SMAX1 to resistance requires SMXL6/7/8. Moreover, the exogenous application of KAR2 enhanced resistance against Pst, but KAR-induced resistance depended on salicylic acid (SA) signaling. Inhibition of karrikin signaling delayed SA-mediated defense responses and inhibited pathogen-induced protein biosynthesis. Together, we propose that the MAX2-KAI2-SMAX1 complex regulates resistance with the assistance of SMXL6/7/8 and SA signaling and that SMAX1/SMXLs possibly form a multimeric complex with their target transcription factors to fine tune immune responses.

[Research on eye movement data classification using support vector machine with improved whale optimization algorithm].

When performing eye movement pattern classification for different tasks, support vector machines are greatly affected by parameters. To address this problem, we propose an algorithm based on the improved whale algorithm to optimize support vector machines to enhance the performance of eye movement data classification. According to the characteristics of eye movement data, this study first extracts 57 features related to fixation and saccade, then uses the ReliefF algorithm for feature selection. To address the problems of low convergence accuracy and easy falling into local minima of the whale algorithm, we introduce inertia weights to balance local search and global search to accelerate the convergence speed of the algorithm and also use the differential variation strategy to increase individual diversity to jump out of local optimum. In this paper, experiments are conducted on eight test functions, and the results show that the improved whale algorithm has the best convergence accuracy and convergence speed. Finally, this paper applies the optimized support vector machine model of the improved whale algorithm to the task of classifying eye movement data in autism, and the experimental results on the public dataset show that the accuracy of the eye movement data classification of this paper is greatly improved compared with that of the traditional support vector machine method. Compared with the standard whale algorithm and other optimization algorithms, the optimized model proposed in this paper has higher recognition accuracy and provides a new idea and method for eye movement pattern recognition. In the future, eye movement data can be obtained by combining it with eye trackers to assist in medical diagnosis.

The WRKY10-VQ8 module safely and effectively regulates rice thermotolerance.

WRKY transcription factors (TFs) play crucial roles in biotic and abiotic stress responses. However, their roles in thermal response are still largely elusive, especially in rice. In this study, we revealed the functions of WRKY10 TF and VQ8 protein containing VQ motif in rice thermotolerance. Overexpression of WRKY10 or loss of VQ8 function increases thermosensitivity, whereas conversely, overexpression of VQ8 or loss of WRKY10 function enhances thermotolerance. Overexpression of WRKY10 accelerates reactive oxygen species (ROS) accumulation in chloroplasts and apoplasts, and it also induces the expression of heat shock TF and protein genes. We also found that WRKY10 regulates nuclear DNA fragmentation and hypersensitive response by modulating NAC4 TF expression. The balance between destructive and protective responses in WRKY10-overexpression plant is more fragile and more easily broken by heat stress compared with wild type. In vitro and in vivo assays revealed that VQ8 interacts with WRKY10 and inhibits the transcription activity via repressing its DNA-binding activity. Our study demonstrates that WRKY10 negatively regulates thermotolerance by modulating the ROS balance and the hypersensitive response and that VQ8 functions antagonistically to positively regulate thermotolerance. The functional module of WRKY10-VQ8 provides safe and effective regulatory mechanisms in the heat stress response.

[Analysis of Characteristic Eye Movement Behaviors of Young Adults with Different Degrees of Myopia].

Objective: To measure the parameters of eye movement of young adult patients of myopia, to compare the differences in the parameters of patient groups with varied degrees of myopia, and to analyze the correlation between eye movement parameters and axial length (AL) and spherical equivalent refraction (SER). Methods: A total of 91 young adult patients of myopia were recruited. The subjects were divided into three groups of low, moderate, and high myopia according to their SER. Information on the subjects' age and sex was collected and general clinical examination was completed. The subjects' binocular fixation, reflexive saccade, and antisaccade were measured with eye tracker. One-way ANOVA or Kruskal-wallis test was used to compare the general data and eye movement parameters of the three groups of myopic patients. Pearson or Spearman correlation analysis was used to analyze AL and SER's correlation with 95% bivariate contour ellipse area (BCEA) and saccadic parameters. Results: The 95% BCEA for mild, moderate, and high myopia groups were 2.08 (0.54, 14.69) deg 2, 4.99 (0.94, 49.22) deg 2, and 5.79 (2.18, 64.91) deg 2, respectively. There was significant difference between the 95% BCEA of the three groups ( P=0.029). The 95% BCEA of the mild myopia group was significantly smaller than that of the high myopia group ( P=0.01). There were no significant differences in saccadic parameters among the three groups ( P>0.05). There was a significant positive correlation between 95% BCEA and AL ( r=0.342, P=0.001). There was a significant negative correlation between 95% BCEA and SER ( r=-0.322, P=0.002). There was no significant correlation between the parameters of saccadic movement and the AL or SER ( P>0.05). Conclusion: For young adult myopic patients, the longer the AL is, the higher the degree of the myopia and the worse the fixation stability becomes. The fixation stability of patients with high myopia was significantly worse than that of patients with mild myopia. There was no significant difference in saccadic parameters in patients with different degrees of myopia.

Arabidopsis SMAX1 overaccumulation suppresses rosette shoot branching and promotes leaf and petiole elongation.

ARABIDOPSIS: SMAX1/SMXL (SUPPRESSOR OF MAX2 1/SMAX1-LIKE) proteins function as transcriptional repressors in karrikin and strigolactone (SL) signaling pathways and regulate plant architecture. MAX2 is a common factor in the two signaling pathways and a component of the SCF complex that modulates the proteasome-mediated degradation of SMAX1/SMXLs. SMXL6, 7, and 8 proteins promote shoot branching and inhibit petiole elongation. Our study found that the accumulation of SMAX1 suppresses rosette shoot branching and increases cauline branches on the primary inflorescence stem, plant height, petiole length, and leaf length/width ratio. The SMAX1 accumulation enhances the expression of BRC1, HB53, HB40, and HB21 that modulate shoot branching. SMAX1 also regulates the expression of the genes involved in auxin transport, cytokinin signaling pathway, and SL biosynthesis. The expression analyses of these genes suggest that excessive SMAX1 should accelerate the transport of auxin and the biosynthesis of SL in plants. High SL concentration suppresses the bud development in smax1D mutant that accumulates SMAX1 protein in plant. However, the effects of cytokinin and auxin on shoot branching remain elusive in the mutant with excessive SMAX1. SMAX1 regulates leaf shape and petiole length via modulating TCP1 expression. Our findings reveal a novel function of SMAX1 and new mechanism of shoot branching.

C-C Bond Cleavage Initiated Cascade Reaction of ß-Enaminones: One-Pot Synthesis of 5-Hydroxy-1H-pyrrol-2(5H)-ones.

An unprecedented C(CO)-C(Ar) bond cleavage of ß-enaminones has been realized under mild and transition-metal-free conditions. The cascade transformation based on this C-C bond cleavage involves 1,3-O/C migration and aerobic hydroxylation and leads to various 5-hydroxy-1H-pyrrol-2(5H)-ones with broad functional group tolerance. The application of this methodology has been showcased by preparing 5-alkoxy-1H-pyrrol-2(5H)-one derivatives and a pyrrolo[2,1-a]isoquinolin-3-one derivative.

Interference with lncRNA NEAT1 promotes the proliferation, migration, and invasion of trophoblasts by upregulating miR-411-5p and inhibiting PTEN expression.

Background: Preeclampsia (PE) is an idiopathic hypertensive disorder of pregnancy, which is related to abnormal placental villi development. Our previous study has found that lncRNA NEAT1 promotes apoptosis of trophoblasts, but the role of NEAT1 in proliferation, migration, and invasion is still unclear. This study explores the role of NEAT1 in proliferation, migration, and invasion of trophoblasts.Methods: NEAT1 and miR-411-5p levels were detected by quantitative real-time PCR. Colony formation assay detected cell proliferation and transwell assay detected cell migration and invasion. Dual-luciferase reporter assay detected the binding between NEAT1 and miR-411-5p as well as the binding between miR-411-5p and PTEN. RNA pull-down assay detected the combination between NEAT1 and miR-411-5p.Result: NEAT1 was increased and miR-411-5p was reduced in PE patients and human trophoblasts (HTR8/SVneo cells) that were induced with H2O2. Interference with NEAT1 promoted cell proliferation, migration, and invasion, and the miR-411-5p inhibitor reversed the effect of siRNA-NEAT1. The expression of PTEN was promoted in PE patients and HTR8/SVneo cells that were induced with H2O2, while the miR-411-5p mimic inhibited PTEN expression, and the plasmid-mediated PTEN overexpression reversed the effect of the miR-411-5p mimic. Besides, under H2O2 induction, the miR-411-5p mimic promoted cell proliferation, migration, and invasion, and the plasmid-mediated PTEN overexpression reversed the effect of the miR-411-5p mimic.Conclusion: Interference with lncRNA NEAT1 promoted the proliferation, migration, and invasion of trophoblasts and alleviated the development of PE, which was partly mediated by upregulating miR-411-5p and inhibiting PTEN expression.

[A Gaussian mixture-hidden Markov model of human visual behavior].

Vision is an important way for human beings to interact with the outside world and obtain information. In order to research human visual behavior under different conditions, this paper uses a Gaussian mixture-hidden Markov model (GMM-HMM) to model the scanpath, and proposes a new model optimization method, time-shifting segmentation (TSS). The TSS method can highlight the characteristics of the time dimension in the scanpath, improve the pattern recognition results, and enhance the stability of the model. In this paper, a linear discriminant analysis (LDA) method is used for multi-dimensional feature pattern recognition to evaluates the rationality and the accuracy of the proposed model. Four sets of comparative trials were carried out for the model evaluation. The first group applied the GMM-HMM to model the scanpath, and the average accuracy of the classification could reach 0.507, which is greater than the opportunity probability of three classification (0.333). The second set of trial applied TSS method, and the mean accuracy of classification was raised to 0.610. The third group combined GMM-HMM with TSS method, and the mean accuracy of classification reached 0.602, which was more stable than the second model. Finally, comparing the model analysis results with the saccade amplitude (SA) characteristics analysis results, the modeling analysis method is much better than the basic information analysis method. Via analyzing the characteristics of three types of tasks, the results show that the free viewing task have higher specificity value and a higher sensitivity to the cued object search task. In summary, the application of GMM-HMM model has a good performance in scanpath pattern recognition, and the introduction of TSS method can enhance the difference of scanpath characteristics. Especially for the recognition of the scanpath of search-type tasks, the model has better advantages. And it also provides a new solution for a single state eye movement sequence.

[Joint optic disc and cup segmentation based on residual multi-scale fully convolutional neural network].

Glaucoma is the leading cause of irreversible blindness, but its early symptoms are not obvious and are easily overlooked, so early screening for glaucoma is particularly important. The cup to disc ratio is an important indicator for clinical glaucoma screening, and accurate segmentation of the optic cup and disc is the key to calculating the cup to disc ratio. In this paper, a full convolutional neural network with residual multi-scale convolution module was proposed for the optic cup and disc segmentation. First, the fundus image was contrast enhanced and polar transformation was introduced. Subsequently, W-Net was used as the backbone network, which replaced the standard convolution unit with the residual multi-scale full convolution module, the input port was added to the image pyramid to construct the multi-scale input, and the side output layer was used as the early classifier to generate the local prediction output. Finally, a new multi-tag loss function was proposed to guide network segmentation. The mean intersection over union of the optic cup and disc segmentation in the REFUGE dataset was 0.904 0 and 0.955 3 respectively, and the overlapping error was 0.178 0 and 0.066 5 respectively. The results show that this method not only realizes the joint segmentation of cup and disc, but also improves the segmentation accuracy effectively, which could be helpful for the promotion of large-scale early glaucoma screening.

Reconstructing 3D point clouds in real time with look-up tables for structured light scanning along both horizontal and vertical directions.

By scanning static, not moving, objects along both the horizontal and vertical axes instead of one, structured light illumination achieves more accurate and robust 3D surface reconstructions but with greater latency on computing 3D point clouds. If scanning is performed along only one axis, it has been reported that look-up tables, manually derived from the calibration matrices of a camera and a projector, can significantly help to speed up computation; however, it has been nearly impossible to manually derive similar look-up tables for phases scanned along two axes. In this Letter, we bridge this divide by introducing the constraint of epipolar geometry to automatically compute look-up tables and thus, significantly speed up computing 3D point clouds with only basic arithmetic operations rather than time-consuming matrix computations. Experimental results show that the proposed method, using only single-thread CPU computing, reduces process latency by an order of magnitude.

Molecular subgroups and B7-H4 expression levels predict responses to dendritic cell vaccines in glioblastoma: an exploratory randomized phase II clinical trial.

Dendritic cell (DC)-based vaccination is a promising approach for active-specific immunotherapy, but is currently of limited efficacy. The safety and effectiveness of a DC vaccine (DCV) loaded with glioblastoma stem cell-like (GSC) antigens was assessed in glioblastoma multiforme (GBM) patients. In this double-blind, placebo-controlled phase II clinical trial, 43 GBM patients were randomized after surgery at a 1:1 ratio to receive either DCV (n = 22) or normal saline placebo (n = 21). Overall survival (OS) and progression-free survival (PFS) were analysed. Participants were stratified into different molecular subgroups based on the mutation (MT) status of isocitrate dehydrogenase (IDH1/2) and telomerase reverse transcriptase (TERT). Plasma cytokine levels, tumor-infiltrating lymphocyte numbers and immune co-inhibitory molecules PD-L1 and B7-H4 were also assessed. Multivariate Cox regression analysis revealed that DCV treatment significantly prolonged OS (p = 0.02) after adjusting for IDH1 and TERT promoter MT and B7-H4 expression, primary vs recurrent GBM. Among IDH1wild type (WT) TERTMT patients, DCV treatment significantly prolonged OS (p < 0.01) and PFS (p = 0.03) and increased plasma levels of cytokines CCL22 and IFN-γ compared with placebo. Patients with low B7-H4 expression showed significantly prolonged OS (p = 0.02) after DCV treatment. Therefore, IDH1WTTERTMT and low B7-H4 expression identified subgroups of GBM patients more responsive to GSC DCV-based specific active-immunotherapy.

[Analysis of genetic polymorphisms of 15 STR loci among ethnic Hans from Xiamen].

OBJECTIVE: To assess the value of 15 short tandem repeat (STR) loci selected by an AmpFLSTR IdentifilerTM system for personal identification and paternity testing among ethnic Hans from Xiamen, Fujian. METHODS: For 400 unrelated individuals, allelic frequencies for the 15 STR loci from the AmpFLSTR IdentifilerTM kit were determined. Population genetics parameters for forensic usage were calculated. RESULTS: No deviation of the observed allele frequency from Hardy-Weinberg equilibrium expectations was found by Chi-square test (P>0.05). All of the 15 loci were highly polymorphic. Observed heterozygosity has varied between 0.580 and 0.868. Matching probability was between 0.036 and 0.148. Power of discrimination was between 0.798 and 0.967. Polymorphic information content was between 0.560 and 0.850. And power of exclusion was between 0.268 and 0.730. CONCLUSION: All of the 15 loci selected by the AmpFLSTR IdentifilerTM system are highly polymorphic among ethnic Hans from Xiamen. By determining the alleles and allelic frequencies, data for genetic polymorphisms usable for paternity testing and personal identification for local population were obtained.

[A computer tomography assisted method for the automatic detection of region of interest in dynamic kidney images].

Glomerular filtration rate (GFR), which can be estimated by Gates method with dynamic kidney single photon emission computed tomography (SPECT) imaging, is a key indicator of renal function. In this paper, an automatic computer tomography (CT)-assisted detection method of kidney region of interest (ROI) is proposed to achieve the objective and accurate GFR calculation. In this method, the CT coronal projection image and the enhanced SPECT synthetic image are firstly generated and registered together. Then, the kidney ROIs are delineated using a modified level set algorithm. Meanwhile, the background ROIs are also obtained based on the kidney ROIs. Finally, the value of GFR is calculated via Gates method. Comparing with the clinical data, the GFR values estimated by the proposed method were consistent with the clinical reports. This automatic method can improve the accuracy and stability of kidney ROI detection for GFR calculation, especially when the kidney function has been severely damaged.

Cerebral blood perfusion changes in amputees with myoelectric hands after rehabilitation: a SPECT computer-aided analysis.

BACKGROUND: Rehabilitation, which is essential for amputees with myoelectric hands, can improve the quality of daily life by remodeling the neuron network. In our study, we aim to develop a cerebral blood perfusion (CBF) single-photon emission computed tomography computer-aided (SPECT-CA) detection scheme to automatically locate the brain's activated regions after rehabilitation. RESULTS: Five participants without forearms (three male, two female, mean age 51 ± 12.89 years, two missing the right side, and three missing the left side) were included in our study. In the clinical assessment, all of the participants received higher scores after training. The results of the SPM analysis indicated that CBF in the precentral gyrus, postcentral gyrus, frontal lobe, temporal lobe and cerebellum was significantly different among the five participants (P < 0.05). Moreover, SPECT-CA showed that the activated brain areas mainly included the precentral gyrus, postcentral gyrus, cerebellum and extensive cerebral cortex. CONCLUSION: Our study demonstrated that the CBF SPECT-CA method can detect the brain blood perfusion changes induced by rehabilitation with high sensitivity and accuracy. This method has great potential for locating the remodeled neuron regions of amputees with myoelectric hands after rehabilitation.

Magnetic resonance imaging findings of a case with Wolffian tumor and related literature review.

Background: Wolffian tumors in females are rare gynecological neoplasms, with fewer than 100 cases reported. Existing literature primarily focuses on the pathology, and reports involving imaging are limited. Objective: This study presents a case of Wolffian tumor, emphasizing its magnetic resonance imaging (MRI) characteristics to enhance preoperative diagnostic accuracy. Case report: A 56-year-old woman presented with a year-long history of irregular vaginal bleeding. MRI revealed a solid mass in the right adnexal region. On T2-weighted images, the mass exhibited slightly elevated signal intensity with a distinctive low-signal intensity rim. Diffusion-weighted imaging displayed markedly increased signal intensity, and the contrast enhancement was moderate. The patient underwent laparoscopic right adnexectomy and received a Wolffian tumor diagnosis. No recurrence was observed during a 6-month follow-up. Conclusions: Wolffian tumors exhibit distinctive MRI presentations. Notably, the prominent low-signal intensity rim on MRI may aid in accurate preoperative tumor diagnosis.