AimsBrachyury overexpression has been reported in various human malignant neoplasms, but its expression and function in hepatocellular carcinoma progression and metastasis remains unknown. The present study aimed to evaluate the critical role of Brachyury in HCC metastasis.MethodsThe expression of Brachyury in human HCC (SMMC7721, HepG2, FHCC98, and Hep3B) and control cell lines was analyzed using quantitative reverse-transcriptase polymerase chain reaction and immunoflourence methods. Cancerous tissues collected from patients with HCC (n = 112) were analyzed using immunohistochemical method; a microarray analysis of HCC tissues was performed to explore the clinicopathological variables of HCC. The migratory and invasive capacities of Brachyury-SMMC7721 and Brachyury-HepG2 transfected cells were evaluated using in vitro scratch wound healing and Matrigel invasion assays, respectively. Further, six-week-old male BALB/c nude mice (n = 10) model was used in vivo assay.ResultsElevated expression of Brachyury was detected in HCCs (62.5%) compared with that in adjacent nontumorous tissues. Clinicopathological analysis revealed a close correlation of Brachyury expression with distant metastasis and poor prognosis of HCC. Overexpression of Brachyury promoted epithelial-mesenchymal transition (EMT) and metastasis of HCC cells in vitro and in vivo. Brachyury overexpression enhanced Akt activation by inhibiting phosphatase and tensin homolog (PTEN), which led to subsequent stabilization of Snail, a critical EMT mediator.ConclusionThe study findings suggest that elevated Brachyury facilitates HCC metastasis by promoting EMT via PTEN/Akt/Snail-dependent pathway. Brachyury plays a pivotal role in HCC metastasis and may serve as a novel prognostic biomarker and therapeutic target.
Galvanic vestibular stimulation (GVS) can be used to study the body's response to vestibular stimuli. This study aimed to investigate whether postural responses to GVS were different between pilots and the general populace. Bilateral bipolar GVS was applied with a constant-current profile to 12 pilots and 12 control subjects via two electrodes placed over the mastoid processes. Both GVS threshold and the center of pressure's trajectory (COP's trajectory) were measured. Position variability of COP during spontaneous body sway and peak displacement of COP during GVS-induced body sway were calculated in the medial-lateral direction. Spontaneous body sway was slight for all subjects, and there was no significant difference in the value of COP position variability between the pilots and controls. Both the GVS threshold and magnitude of GVS-induced body deviation were similar for different GVS polarities. GVS thresholds were similar between the two groups, but the magnitude of GVS-induced body deviation in the controls was significantly larger than that in the pilots. The pilots showed less GVS-induced body deviation, meaning that pilots may have a stronger ability to suppress vestibular illusions.
Many structures in civil engineering are symmetrical. Crack detection is a critical task in the monitoring and inspection of civil engineering structures. This study implements a lightweight neural network based on the YOLOv4 algorithm to detect concrete surface cracks. In the extraction of backbone and the design of neck and head, the symmetry concept is adopted. The model modules are improved to reduce the depth and complexity of the overall network structure. Meanwhile, the separable convolution is used to realize spatial convolution, and the SPP and PANet modules are improved to reduce the model parameters. The convolutional layer and batch normalization layer are merged to improve the model inference speed. In addition, using the focal loss function for reference, the loss function of object detection network is improved to balance the proportion of the cracks and the background samples. To comprehensively evaluate the performance of the improved method, 10,000 images (256 × 256 pixels in size) of cracks on concrete surfaces are collected to build the database. The improved YOLOv4 model achieves an mAP of 94.09% with 8.04 M and 0.64 GMacs. The results show that the improved model is satisfactory in mAP, and the model size and calculation amount are greatly reduced. This performs better in terms of real-time detection on concrete surface cracks.
Abstract. Unrecognized spatial disorientation (SD) which is intimately linked with brain cognitive function is always a fatal issue for the safety of pilots. To explore its effects on human brain cognitive functions, electroencephalography (EEG) functional network analysis methods were adopted to examine topological changes in the connection of cognitive regions when experiencing unrecognized SD. Twelve male pilots participated in the study. They were subjected to a SD scene, namely visual rotation, which evoked unrecognized SD. For the main EEG frequency intervals, the phase lag index (PLI) and normalized mutual information (NMI) were calculated to quantify the EEG data. Then weighted connectivity networks were constructed and their properties were characterized in terms of an average clustering coefficient and global efficiency. A T-test was performed to compare PLI, NMI and network measures under unrecognized SD and non-SD conditions. It indicated a weak functional connectivity level in the theta band under unrecognized SD based on the significant decrease of mean values of PLI and NMI (p<0.05). Meanwhile, both the average clustering coefficient and global efficiency in the theta band reduced under the unrecognized SD condition. The decrease of the average clustering coefficient and global efficiency demonstrates a lack of small-world characteristics and a decline in processing efficiency of brain cognitive regions. All the experimental results show that unrecognized SD may have a negative effect on brain functional networks in the theta band. S1115With the development of aviation and space technology, astronauts and pilots are being sent into space on board various aircrafts and planes. Meanwhile, a series of problems is emerging, and spatial disorientation (SD) is one of these problems that have to be studied. During flight, three sensory systems named visual, vestibular and proprioceptive system often send conflicting information to the brain, and then three types of SD may happen, namely recognized SD, unrecognized SD and incapacitating SD [1]. SD often causes flying accidents, and statistics show that from 5% to 10% of all aviation accidents can be attributed to SD, while 90% of these accidents are fatal [2]. One thing to note is that accidents caused by unrecognized SD account for 80% of the total caused by SD [3]. The brain is considered to be a complex system, comprising spatially interconnected areas [4]. Similar with the connectivity of complex network, the structural and functional brain network possesses the small-world properties found by Watts and Strogatz [5]. It has been suggested that small-world properties support rapid adaptive reconfiguration of functional connectivity in response to varying cognitive demands [6] when listening to music [4] and learning [7]. Also many researches indicate that network measures show a great change in neurological diseases like Alzheimer's [8] and schizophrenia [9]. These findings indicate that the brain functional network has a dynamic feature according to...
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