Protein structuromics: A new method for protein structure-function crosstalk in glioma.

Glioma is a type of tumor that starts in the glial cells of the brain or spine. Since the 1800s, when the disease was first named, its survival rates have always been unsatisfactory. Despite great advances in molecular biology and traditional treatment methods, many questions regarding cancer occurrence and the underlying mechanism remain to be answered. In this study, we assessed the protein structural features of 20 oncogenes and 20 anti-oncogenes via protein structure and dynamic analysis methods and 3D structural and systematic analyses of the structure-function relationships of proteins. All of these results directly indicate that unfavorable group proteins show more complex structures than favorable group proteins. As the tumor cell microenvironment changes, the balance of oncogene-related and anti-oncogene-related proteins is disrupted, and most of the structures of the two groups of proteins will be disrupted. However, more unfavorable group proteins will maintain and refold to achieve their correct shape faster and perform their functions more quickly than favorable group proteins, and the former thus support cancer development. We hope that these analyses will help promote mechanistic research and the development of new treatments for glioma.

Prevalence and impact of viral myocarditis in patients with severe fever with thrombocytopenia syndrome.

To explore the association and impact between viral myocarditis and mortality in patients with severe fever with thrombocytopenia syndrome. A dynamic analysis was conducted between fatal group and nonfatal group regarding the daily epidemiology data, clinical symptoms, and electrocardiogram (ECG), echocardiogram, and laboratory findings. Outcomes of patients with and without viral myocarditis were compared. The association between viral myocarditis and mortality was analyzed. Among 183 severe fever with thrombocytopenia syndrome patients, 32 were in the fatal group and 151 in the nonfatal group; there were 26 (81.25%) with viral myocarditis in the fatal group, 66 (43.70%) with viral myocarditis in the nonfatal group (p < 0.001), 79.35% of patients had abnormal ECG results. The abnormal rate of ECG in the fatal group was 100%, and in the nonfatal group was 74.83%. Univariate analysis found that the number of risk factors gradually increased on Day 7 of the disease course and reached the peak on Day 10. Combined with the dynamic analysis of the disease course, alanine aminotransferase, aspartate aminotransferase, creatine kinase, creatine kinase fraction, lactate dehydrogenase, hydroxybutyrate dehydrogenase, neutrophil count, serum creatinine, Na, Ca, carbon dioxide combining power, amylase, lipase, activated partial thromboplastin time and thrombin time had statistically significant impact on prognosis. The incidence of fever with thrombocytopenia syndrome combined with viral myocarditis is high, especially in the fatal group of patients. Viral myocarditis is closely related to prognosis and is an early risk factor. The time point for changes in myocarditis is Day 7 of the course of the disease.

Novel PATL2 variants cause female infertility with oocyte maturation defect.

PURPOSE: Oocyte maturation defect (OOMD) is a rare cause of in vitro fertilization failure characterized by the production of immature oocytes. Compound heterozygous or homozygous PATL2 mutations have been associated with oocyte arrest at the germinal vesicle (GV), metaphase I (MI), and metaphase II (MII) stages, as well as morphological changes. METHODS: In this study, we recruited three OOMD cases and conducted a comprehensive multiplatform laboratory investigation. RESULTS: Whole exome sequence (WES) revealed four diagnostic variants in PATL2, nonsense mutation c.709C > T (p.R237*) and frameshift mutation c.1486_1487delinsT (p.A496Sfs*4) were novel mutations that have not been reported previously. Furthermore, the pathogenicity of these variants was predicted using in silico analysis, which indicated detrimental effects. Molecular dynamic analysis suggested that the A496S variant disrupted the hydrophobic segment, leading to structural changes that affected the overall protein folding and stability. Additionally, biochemical and molecular experiments were conducted on cells transfected with wild-type (WT) or mutant PATL2 (p.R237* and p.A496Sfs*4) plasmid vectors. CONCLUSIONS: The results demonstrated that PATL2A496Sfs*4 and PATL2R237* had impacts on protein size and expression level. Interestingly, expression levels of specific genes involved in oocyte maturation and early embryonic development were found to be simultaneously deregulated. The findings in our study expand the variation spectrum of the PATL2 gene, provide solid evidence for counseling on future pregnancies in affected families, strongly support the application of in the diagnosis of OOMD, and contribute to the understanding of PATL2 function.

Application of Stereo Digital Image Correlation on Facial Expressions Sensing.

Facial expression is an important way to reflect human emotions and it represents a dynamic deformation process. Analyzing facial movements is an effective means of understanding expressions. However, there is currently a lack of methods capable of analyzing the dynamic details of full-field deformation in expressions. In this paper, in order to enable effective dynamic analysis of expressions, a classic optical measuring method called stereo digital image correlation (stereo-DIC or 3D-DIC) is employed to analyze the deformation fields of facial expressions. The forming processes of six basic facial expressions of certain experimental subjects are analyzed through the displacement and strain fields calculated by 3D-DIC. The displacement fields of each expression exhibit strong consistency with the action units (AUs) defined by the classical Facial Action Coding System (FACS). Moreover, it is shown that the gradient of the displacement, i.e., the strain fields, offers special advantages in characterizing facial expressions due to their localized nature, effectively sensing the nuanced dynamics of facial movements. By processing extensive data, this study demonstrates two featured regions in six basic expressions, one where deformation begins and the other where deformation is most severe. Based on these two regions, the temporal evolutions of the six basic expressions are discussed. The presented investigations demonstrate the superior performance of 3D-DIC in the quantitative analysis of facial expressions. The proposed analytical strategy might have potential value in objectively characterizing human expressions based on quantitative measurement.

Channel Features and API Frequency-Based Transformer Model for Malware Identification.

Malicious software (malware), in various forms and variants, continues to pose significant threats to user information security. Researchers have identified the effectiveness of utilizing API call sequences to identify malware. However, the evasion techniques employed by malware, such as obfuscation and complex API call sequences, challenge existing detection methods. This research addresses this issue by introducing CAFTrans, a novel transformer-based model for malware detection. We enhance the traditional transformer encoder with a one-dimensional channel attention module (1D-CAM) to improve the correlation between API call vector features, thereby enhancing feature embedding. A word frequency reinforcement module is also implemented to refine API features by preserving low-frequency API features. To capture subtle relationships between APIs and achieve more accurate identification of features for different types of malware, we leverage convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. Experimental results demonstrate the effectiveness of CAFTrans, achieving state-of-the-art performance on the mal-api-2019 dataset with an F1 score of 0.65252 and an AUC of 0.8913. The findings suggest that CAFTrans improves accuracy in distinguishing between various types of malware and exhibits enhanced recognition capabilities for unknown samples and adversarial attacks.

Design, Fabrication, and Dynamic Analysis of a MEMS Ring Resonator Supported by Twin Circular Curve Beams.

In this paper, we present a compressive study on the design and development of a MEMS ring resonator and its dynamic behavior under electrostatic force when supported by twin circular curve beams. Finite element analysis (FEA)-based modeling techniques are used to simulate and refine the resonator geometry and transduction. In proper FEA or analytical modeling, the explicit description and accurate values of the effective mass and stiffness of the resonator structure are needed. Therefore, here we outlined an analytical model approach to calculate those values using the first principles of kinetic and potential energy analyses. The natural frequencies of the structure were then calculated using those parameters and compared with those that were simulated using the FEA tool ANSYS. Dynamic analysis was performed to calculate the pull-in voltage, shift of resonance frequency, and harmonic analyses of the ring to understand how the ring resonator is affected by the applied voltage. Additional analysis was performed for different orientations of silicon and assessing the frequency response and frequency shifts. The prototype was fabricated using the standard silicon-on-insulator (SOI)-based MEMS fabrication process and the experimental results for resonances showed good agreement with the developed model approach. The model approach presented in this paper can be used to provide valuable insights for the optimization of MEMS resonators for various operating conditions.

A Wireless Data Acquisition System Based on MEMS Accelerometers for Operational Modal Analysis of Bridges.

This paper illustrates a novel and cost-effective wireless monitoring system specifically developed for operational modal analysis of bridges. The system employs battery-powered wireless sensors based on MEMS accelerometers that dynamically balance power consumption with high processing features and a low-power, low-cost Wi-Fi module that ensures operation for at least five years. The paper focuses on the system's characteristics, stressing the challenges of wireless communication, such as data preprocessing, synchronization, system lifetime, and simple configurability, achieved through the integration of a user-friendly, web-based graphical user interface. The system's performance is validated by a lateral excitation test of a model structure, employing dynamic identification techniques, further verified through FEM modeling. Later, a system composed of 30 sensors was installed on a concrete arch bridge for continuous OMA to assess its behavior. Furthermore, emphasizing its versatility and effectiveness, displacement is estimated by employing conventional and an alternative strategy based on the Kalman filter.

Rapid dynamics of general transcription factor TFIIB binding during preinitiation complex assembly revealed by single-molecule analysis.

Transcription of protein-encoding genes in eukaryotic cells requires the coordinated action of multiple general transcription factors (GTFs) and RNA polymerase II (Pol II). A "step-wise" preinitiation complex (PIC) assembly model has been suggested based on conventional ensemble biochemical measurements, in which protein factors bind stably to the promoter DNA sequentially to build a functional PIC. However, recent dynamic measurements in live cells suggest that transcription factors mostly interact with chromatin DNA rather transiently. To gain a clearer dynamic picture of PIC assembly, we established an integrated in vitro single-molecule transcription platform reconstituted from highly purified human transcription factors and complemented it by live-cell imaging. Here we performed real-time measurements of the hierarchal promoter-specific binding of TFIID, TFIIA, and TFIIB. Surprisingly, we found that while promoter binding of TFIID and TFIIA is stable, promoter binding by TFIIB is highly transient and dynamic (with an average residence time of 1.5 sec). Stable TFIIB-promoter association and progression beyond this apparent PIC assembly checkpoint control occurs only in the presence of Pol II-TFIIF. This transient-to-stable transition of TFIIB-binding dynamics has gone undetected previously and underscores the advantages of single-molecule assays for revealing the dynamic nature of complex biological reactions.

Dynamics and concordance alterations of regional brain function indices in vestibular migraine: a resting-state fMRI study.

BACKGROUND: Prior MRI studies on vestibular migraine (VM) have revealed abnormalities in static regional intrinsic brain activity (iBA) and dynamic functional connectivity between brain regions or networks. However, the temporal variation and concordance of regional iBA measures remain to be explored. METHODS: 57 VM patients during the interictal period were compared to 88 healthy controls (HC) in this resting-state functional magnetic resonance imaging (fMRI) study. The dynamics and concordance of regional iBA indices, including amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo), were examined by utilizing sliding time-window analysis. Partial correlation analyses were performed between clinical parameters and resting-state fMRI indices in brain areas showing significant group differences. RESULTS: The VM group showed increased ALFF and ReHo dynamics, as well as increased temporal concordance between ALFF and ReHo in the bilateral paracentral lobule and supplementary motor area relative to the HC group. We also found decreased ReHo dynamics in the right temporal pole, and decreased ALFF dynamics in the right cerebellum posterior lobe, bilateral angular gyrus and middle occipital gyrus (MOG) in the VM group compared with the HC group. Moreover, a positive correlation was observed between ALFF dynamics in the left MOG and vertigo disease duration across all VM patients. CONCLUSION: Temporal dynamics and concordance of regional iBA indices were altered in the motor cortex, cerebellum, occipital and temporoparietal cortex, which may contribute to disrupted multisensory processing and vestibular control in patients with VM. ALFF dynamics in the left MOG may be useful biomarker for evaluating vertigo burden in this disorder.

Differentiating high-grade glioma progression from treatment-related changes with dynamic [18F]FDOPA PET: a multicentric study.

OBJECTIVES: Diagnostic accuracy of amino-acid PET for distinguishing progression from treatment-related changes (TRC) is currently based on single-center non-homogeneous glioma populations. Our study assesses the diagnostic value of static and dynamic [18F]FDOPA PET acquisitions to differentiate between high-grade glioma (HGG) recurrence and TRC in a large cohort sourced from two independent nuclear medicine centers. METHODS: We retrospectively identified 106 patients with suspected glioma recurrences (WHO GIII, n = 38; GIV, n = 68; IDH-mutant, n = 35, IDH-wildtype, n = 71). Patients underwent dynamic [18F]FDOPA PET/CT (n = 83) or PET/MRI (n = 23), and static tumor-to-background ratios (TBRs), metabolic tumor volumes and dynamic parameters (time to peak and slope) were determined. The final diagnosis was either defined by histopathology or a clinical-radiological follow-up at 6 months. Optimal [18F]FDOPA PET parameter cut-offs were obtained by receiver operating characteristic analysis. Predictive factors and clinical parameters were assessed using univariate and multivariate Cox regression survival analyses. RESULTS: Surgery or the clinical-radiological 6-month follow-up identified 71 progressions and 35 treatment-related changes. TBRmean, with a threshold of 1.8, best-differentiated glioma recurrence/progression from post-treatment changes in the whole population (sensitivity 82%, specificity 71%, p < 0.0001) whereas curve slope was only significantly different in IDH-mutant HGGs (n = 25). In survival analyses, MTV was a clinical independent predictor of progression-free and overall survival on the multivariate analysis (p ≤ 0.01). A curve slope > -0.12/h was an independent predictor for longer PFS in IDH-mutant HGGs CONCLUSION: Our multicentric study confirms the high accuracy of [18F]FDOPA PET to differentiate recurrent malignant gliomas from TRC and emphasizes the diagnostic and prognostic value of dynamic acquisitions for IDH-mutant HGGs. KEY POINTS: • The diagnostic accuracy of dynamic amino-acid PET, for distinguishing progression from treatment-related changes, is currently based on single-center non-homogeneous glioma populations. • This multicentric study confirms the high accuracy of static [18F]FDOPA PET images for differentiating progression from treatment-related changes in a homogeneous population of high-grade gliomas and highlights the diagnostic and prognostic value of dynamic acquisitions for IDH-mutant high-grade gliomas. • Dynamic acquisitions should be performed in IDH-mutant glioma patients to provide valuable information for the differential diagnosis of recurrence and treatment-related changes.

Dynamic Analysis of the Coracohumeral Ligament Using Ultra-Sonography in Shoulder Contracture.

The coracohumeral ligament (CHL) is related to the range of motion of the shoulder joint. The evaluation of the CHL using ultrasonography (US) has been reported on the elastic modulus and thickness of the CHL, but no dynamic evaluation method has been established. We aimed to quantify the movement of the CHL by applying Particle Image Velocimetry (PIV), a technique used in the field of fluid engineering, to cases of shoulder contracture using the US. The subjects were eight patients, with 16 shoulders. The coracoid process was identified from the body surface, and a long-axis US image of the CHL parallel to the subscapularis tendon was drawn. The shoulder joint was moved from 0 degrees of internal/external rotation to 60 degrees of internal rotation at a rhythm of one reciprocation every 2 s. The velocity of the CHL movement was quantified by the PIV method. The mean magnitude velocity of CHL was significantly faster on the healthy side. The maximum magnitude velocity was significantly faster on the healthy side. The results suggest that the PIV method is helpful as a dynamic evaluation method, and in patients with shoulder contracture, the CHL velocity was significantly decreased.

Nonlinear Dynamic Analysis of a Piezoelectric Energy Harvester with Mechanical Plucking Mechanism.

In this study, we propose an analytical approach based on the modified differential transform method to investigate the dynamic behavior of a plucking energy harvester. The harvester consists of a piezoelectric cantilever oscillator and a rotating plectrum. The analytical approach provides a closed-form solution that helps determine the starting and ending points of the contact phase between the piezoelectric cantilever and the plectrum. This analytical approach is valuable for simulating complex dynamic interferences in multiple or periodic plucking processes. To evaluate the effects of plucking speed and overlap length of the plectrum on single and periodic plucking, a series of simulations were carried out. The output voltage of the piezoelectric energy harvester increases as the overlap length of the plectrum increases. On the other hand, increasing the plucking speed tends to amplify the magnitude of the contact force while reducing the duration of the contact phase. Therefore, it is crucial to optimize the plucking speed to achieve the maximum linear impulse. For periodic plucking, successful synchronization between the motions of the piezoelectric energy harvester and the rotating plectrum must occur within a limited contact zone. Otherwise, dynamic interferences often cause the plectrum to fail to pluck the energy harvester exactly within the contact zone. Additionally, reducing the plucking speed of the plectrum and increasing the overlap length would be more advantageous for successful periodic-plucking energy harvesting.

Detection of Wheel Polygonization Based on Wayside Monitoring and Artificial Intelligence.

This research presents an approach based on artificial intelligence techniques for wheel polygonization detection. The proposed methodology is tested with dynamic responses induced on the track by passing a Laagrss-type rail vehicle. The dynamic response is attained considering the application of a train-track interaction model that simulates the passage of the train over a set of accelerometers installed on the rail and sleepers. This study, which considers an unsupervised methodology, aims to compare the performance of two feature extraction techniques, namely the Autoregressive Exogenous (ARX) model and Continuous Wavelets Transform (CWT). The extracted features are then submitted to data normalization considering the Principal Component Analysis (PCA) applied to suppress environmental and operational effects. Next to data normalization, data fusion using Mahalanobis distance is performed to enhance the sensitivity to the recognition of defective wheels. Finally, an outlier analysis is employed to distinguish a healthy wheel from a defective one. Moreover, sensitivity analysis is performed to analyze the influence of the number of sensors and their location on the accuracy of the wheel defect detection system.

RBEF: Ransomware Efficient Public Blockchain Framework for Digital Healthcare Application.

These days, the use of digital healthcare has been growing in practice. Getting remote healthcare services without going to the hospital for essential checkups and reports is easy. It is a cost-saving and time-saving process. However, digital healthcare systems are suffering from security and cyberattacks in practice. Blockchain technology is a promising technology that can process valid and secure remote healthcare data among different clinics. However, ransomware attacks are still complex holes in blockchain technology and prevent many healthcare data transactions during the process on the network. The study presents the new ransomware blockchain efficient framework (RBEF) for digital networks, which can identify transaction ransomware attacks. The objective is to minimize transaction delays and processing costs during ransomware attack detection and processing. The RBEF is designed based on Kotlin, Android, Java, and socket programming on the remote process call. RBEF integrated the cuckoo sandbox static and dynamic analysis application programming interface (API) to handle compile-time and runtime ransomware attacks in digital healthcare networks. Therefore, code-, data-, and service-level ransomware attacks are to be detected in blockchain technology (RBEF). The simulation results show that the RBEF minimizes transaction delays between 4 and 10 min and processing costs by 10% for healthcare data compared to existing public and ransomware efficient blockchain technologies healthcare systems.

MDABP: A Novel Approach to Detect Cross-Architecture IoT Malware Based on PaaS.

With the development of internet technology, the Internet of Things (IoT) has been widely used in several aspects of human life. However, IoT devices are becoming more vulnerable to malware attacks due to their limited computational resources and the manufacturers' inability to update the firmware on time. As IoT devices are increasing rapidly, their security must classify malicious software accurately; however, current IoT malware classification methods cannot detect cross-architecture IoT malware using system calls in a particular operating system as the only class of dynamic features. To address these issues, this paper proposes an IoT malware detection approach based on PaaS (Platform as a Service), which detects cross-architecture IoT malware by intercepting system calls generated by virtual machines in the host operating system acting as dynamic features and using the K Nearest Neighbors (KNN) classification model. A comprehensive evaluation using a 1719 sample dataset containing ARM and X86-32 architectures demonstrated that MDABP achieves 97.18% average accuracy and a 99.01% recall rate in detecting samples in an Executable and Linkable Format (ELF). Compared with the best cross-architecture detection method that uses network traffic as a unique type of dynamic feature with an accuracy of 94.5%, practical results reveal that our method uses fewer features and has higher accuracy.

A Survey of the Security Analysis of Embedded Devices.

Embedded devices are pervasive nowadays with the rapid development of the Internet of Things (IoT). This brings significant security issues that make the security analysis of embedded devices important. This paper presents a survey on the security analysis research of embedded devices. First, we analyze the embedded device types and their operating systems. Then, we describe a major dynamic security analysis method for an embedded device, i.e., simulating the firmware of the embedded device and performing fuzzing on the web interface provided by the firmware. Third, we discuss some other issues in embedded security analysis, such as analyzing the attack surface, applying static analysis, and performing large-scale analysis. Based on these analyses, we finally conclude three challenges in the current research and present our insights for future research directions.

Analysis of Postmortem Intestinal Microbiota Successional Patterns with Application in Postmortem Interval Estimation.

Microorganisms play a vital role in the decomposition of vertebrate remains in natural nutrient cycling, and the postmortem microbial succession patterns during decomposition remain unclear. The present study used hierarchical clustering based on Manhattan distances to analyze the similarities and differences among postmortem intestinal microbial succession patterns based on microbial 16S rDNA sequences in a mouse decomposition model. Based on the similarity, seven different classes of succession patterns were obtained. Generally, the normal intestinal flora in the cecum was gradually decreased with changes in the living conditions after death, while some facultative anaerobes and obligate anaerobes grew and multiplied upon oxygen consumption. Furthermore, a random forest regression model was developed to predict the postmortem interval based on the microbial succession trend dataset. The model demonstrated a mean absolute error of 20.01 h and a squared correlation coefficient of 0.95 during 15-day decomposition. Lactobacillus, Dubosiella, Enterococcus, and the Lachnospiraceae NK4A136 group were considered significant biomarkers for this model according to the ranked list. The present study explored microbial succession patterns in terms of relative abundances and variety, aiding in the prediction of postmortem intervals and offering some information on microbial behaviors in decomposition ecology.

Analysis of Dynamic Network Reconfiguration in Adults with Attention-Deficit/Hyperactivity Disorder Based Multilayer Network.

Attention-deficit/hyperactivity disorder (ADHD) has been reported exist abnormal topology structure in the brain network. However, these studies often treated the brain as a static monolithic structure, and dynamic characteristics were ignored. Here, we investigated how the dynamic network reconfiguration in ADHD patients differs from that in healthy people. Specifically, we acquired resting-state functional magnetic resonance imaging data from a public dataset including 40 ADHD patients and 50 healthy people. A novel model of a "time-varying multilayer network" and metrics of recruitment and integration were applied to describe group differences. The results showed that the integration scores of ADHD patients were significantly lower than those of controls at every level. The recruitment scores were lower than healthy people except for the whole-brain level. It is worth noting that the subcortical network and the thalamus in ADHD patients exhibited reduced alliance preference both within and between functional networks. In addition, we also found that recruitment and integration coefficients showed a significant correlation with symptom severity in some regions. Our results demonstrate that the capability to communicate within or between some functional networks is impaired in ADHD patients. These evidences provide a new opportunity for studying the characteristics of ADHD brain networks.

Exploring the cause of the dual allosteric targeted inhibition attaching to allosteric sites enhancing SHP2 inhibition.

SHP2 is a protein tyrosine phosphatase (PTP) that can regulate the tyrosine phosphorylation level. Overexpression of SHP2 will promote the development of cancer diseases, so SHP2 has become one of the popular targets for the treatment of cancer. Studies have reported that both SHP099 and SHP844 are inhibitors of SHP2 and bind to different allosteric sites 1 and 2, respectively. Studies have shown that combining SHP099 with SHP844 will enhance pharmacological pathway inhibition in cells. This study uses molecular dynamic simulations to explore the dual allosteric targeted inhibition mechanism. The result shows that the residues THR108-TRP112 (allosteric site 1) move to LEU236-GLN245 (αB-αC link loop in PTP domain) , the residues of GLN79-GLN87 (allosteric site 2) get close to LEU262-GLN269 (αA-αB link loop in PTP domain) and HIS458-ARG465 (P-loop) come near to ARG501-THR507 (Q-loop) in SHP2-SHP099-SHP844 system, which makes the "inactive conformation" more stable and prevents the substrate from entering the catalytic site. Meanwhile, residue GLU110 (allosteric site 1), ARG265 (allosteric site 2), and ARG501 (Q-loop) are speculated to be the key residues that causing the SHP2 protein in auto-inhibition conformation. It is hoped that this study will provide clues for the development of the dual allosteric targeted inhibition of SHP2.

Motion Analysis of Triangular Fibrocartilage Complex by Using Ultrasonography Images: Preliminary Analysis.

The triangular fibrocartilage complex (TFCC) is a significant stabilizer of the distal radioulnar joint. Diagnosing TFCC injury is currently difficult, but ultrasonography (US) has emerged as a low-cost, minimally invasive diagnostic tool. We aimed to quantitatively analyze TFCC by performing motion analysis by using US. Twelve healthy volunteers, comprising 24 wrists (control group), and 15 patients with TFCC Palmer type 1B injuries (injury group) participated. The US transducer was positioned between the ulnar styloid process and triquetrum and was tilted ulnarly 30° from the vertical line. The wrist was then actively moved from 10° of radial deviation to 20° of ulnar deviation in a 60-rounds-per-minute rhythm that was paced by a metronome. The articular disc displacement velocity magnitude was analyzed by using particle image velocimetry fluid measurement software. The mean area of the articular discs was larger on ulnar deviation in the control group. The mean articular disc area on radial deviation was larger in the injury group. The average articular disc velocity magnitude for the injury group was significantly higher than that for the control group. The results suggest that patients with TFCC injury lose articular disc cushioning and static stability, and subsequent abnormal motion can be analyzed by using US.