Malignant brain tumors have been a serious threat to human health worldwide. This study aims to investigate the role of miR-136-3p in glioma development.
Hematoxylin-eosin staining (H&E) staining was ...used to determine the pathologic alterations of glioma tissues. Quantitative real-time PCR (qRT-PCR) analysis and GEO2R analysis was performed to examine the expression of miRNAs and genes. Western blot was applied to detect the protein expression. Cell counting kit-8 (CCK-8) and colony formation were used to analyze the glioma cell growth. Trans-well assay was used to determine the cell migration. Annexin V-FITC/PI staining was conducted to determine the cell apoptosis of transfected glioma cells. The dual-luciferase reporter assay was carried out to confirm the binding sites of miR-136-3p on 3' untranslated regions (3' UTR) of Kruppel-like factor 7 (KLF7). Tumor-bearing experiment in nude mice was performed to comprehensively investigate the role of miR-136-3p/KLF7 axis in gliomas.
Firstly, the results showed that miR-136-3p was decreased in glioma tissues compared with adjacent tissues. Overexpression of miR-136-3p significantly inhibited cell growth of LN-229 and U251 by decreasing expression of Cyclin A1 and PCNA (proliferating cell nuclear antigen), and it suppressed glioma cell migration by downregulating N-cadherin and elevating E-cadherin levels, and it also promotes glioma cell apoptosis by promoting Bcl2-associated X (Bax) expression but suppressing Bcl-2 expression. Furthermore, we observed that KLF7 was a direct target of miR-136-3p, and KLF7 was negatively regulated by miR-136-3p in glioma cells. Finally, overexpression of KLF7 partly blocked miR-136-3p-induced inhibition of tumor growth in vitro and in vivo.
Targeting miR-136-3p/KLF7 axis might be a novel manner to counter against gliomas.
Vessel segmentation is a key step for various medical applications. This paper introduces the deep learning architecture to improve the performance of retinal vessel segmentation. Deep learning ...architecture has been demonstrated having the powerful ability in automatically learning the rich hierarchical representations. In this paper, we formulate the vessel segmentation to a boundary detection problem, and utilize the fully convolutional neural networks (CNNs) to generate a vessel probability map. Our vessel probability map distinguishes the vessels and background in the inadequate contrast region, and has robustness to the pathological regions in the fundus image. Moreover, a fully-connected Conditional Random Fields (CRFs) is also employed to combine the discriminative vessel probability map and long-range interactions between pixels. Finally, a binary vessel segmentation result is obtained by our method. We show that our proposed method achieve a state-of-the-art vessel segmentation performance on the DRIVE and STARE datasets.
The doubly salient electromagnetic generator (DSEG) has the advantages of flexible control, high robustness, and low cost. Since multiple stator poles share a common set of field windings, the ...magnetic circuit of each phase is asymmetrical, which limits the output performance of the DSEG. The three-phase current balance control for the DSEG system was proposed. By investigating the current waveform under the angular position control (APC), a three-phase current prediction method was proposed. The conduction angle of the power switches in the active rectifier was predicted to make the three-phase current balanced. Based on the analysis and experiment, the current waveform was further analyzed, and the effectiveness of the three-phase current balance control was verified. Compared with the APC method, the output power capacity was improved using the three-phase current balance control under a limited winding current density. The maximum potential of the DSEG was explored, and the electromagnetic load capacity of the DSEG was fully released.
Glaucoma is a chronic eye disease that leads to vision loss. As it cannot be cured, detecting the disease in time is important. Current tests using intraocular pressure (IOP) are not sensitive enough ...for population based glaucoma screening. Optic nerve head assessment in retinal fundus images is both more promising and superior. This paper proposes optic disc and optic cup segmentation using superpixel classification for glaucoma screening. In optic disc segmentation, histograms, and center surround statistics are used to classify each superpixel as disc or non-disc. A self-assessment reliability score is computed to evaluate the quality of the automated optic disc segmentation. For optic cup segmentation, in addition to the histograms and center surround statistics, the location information is also included into the feature space to boost the performance. The proposed segmentation methods have been evaluated in a database of 650 images with optic disc and optic cup boundaries manually marked by trained professionals. Experimental results show an average overlapping error of 9.5% and 24.1% in optic disc and optic cup segmentation, respectively. The results also show an increase in overlapping error as the reliability score is reduced, which justifies the effectiveness of the self-assessment. The segmented optic disc and optic cup are then used to compute the cup to disc ratio for glaucoma screening. Our proposed method achieves areas under curve of 0.800 and 0.822 in two data sets, which is higher than other methods. The methods can be used for segmentation and glaucoma screening. The self-assessment will be used as an indicator of cases with large errors and enhance the clinical deployment of the automatic segmentation and screening.
Angle-closure glaucoma is a major cause of irreversible visual impairment and can be identified by measuring the anterior chamber angle (ACA) of the eye. The ACA can be viewed clearly through ...anterior segment optical coherence tomography (AS-OCT), but the imaging characteristics and the shapes and locations of major ocular structures can vary significantly among different AS-OCT modalities, thus complicating image analysis. To address this problem, we propose a data-driven approach for automatic AS-OCT structure segmentation, measurement, and screening. Our technique first estimates initial markers in the eye through label transfer from a hand-labeled exemplar data set, whose images are collected over different patients and AS-OCT modalities. These initial markers are then refined by using a graph-based smoothing method that is guided by AS-OCT structural information. These markers facilitate segmentation of major clinical structures, which are used to recover standard clinical parameters. These parameters can be used not only to support clinicians in making anatomical assessments, but also to serve as features for detecting anterior angle closure in automatic glaucoma screening algorithms. Experiments on Visante AS-OCT and Cirrus high-definition-OCT data sets demonstrate the effectiveness of our approach.
Most electric vehicles adopt cooperative braking systems that can blend friction braking torque with regenerative braking torque to achieve higher energy efficiency while maintaining a certain ...braking performance and driving safety. This paper presented a new cooperative regenerative braking system that contained a fully-decoupled hydraulic braking mechanism based on a modified electric stability control system. The pressure control algorithm and brake force distribution strategy were also discussed. Dynamic models of a front wheel drive electric car equipped with this system and a simulation platform with a driver model and driving cycles were established. Tests to evaluate the braking performance and energy regeneration were simulated and analyzed on this platform and the simulation results showed the feasibility and effectiveness of this system.
Inward rectifier potassium channels (IK1, Kir) are known to play critical roles in arrhythmogenesis. Thus, how IK1 agonist affects reperfusion arrhythmias needs to be clarified, and its underlying ...mechanisms should be determined. Reperfusion arrhythmias were modeled by coronary ligation (ischemia, 15 minutes) and release (reperfusion, 15 minutes). Zacopride (1.5-50 μg/kg in vivo, or 0.1-10 μmol/Lex vivo) was applied in the settings of pretreatment (3 minutes before coronary ligation) and posttreatment (5 minutes after coronary ligation). Hypoxia (45 minutes) /reoxygenation (30 minutes) model was established in cultured H9c2 (2-1) cardiomyocytes. Zacopride or KN93 was applied before hypoxia (pretreatment). In the setting of pre- or posttreatment, zacopride at 15 μg/kg in vivo or 1 μmol/Lin vitro exhibited superlative protections on reperfusion arrhythmias or intracellular calcium overload. Western blot data from ex vivo hearts or H9c2 (2-1) cardiomyocytes showed that I/R (H/R) induced the inhibition of Kir2.1 (the dominant subunit of IK1 channel in ventricle), phosphorylation and oxidation of CaMKII, downregulation of SERCA2, phosphorylation of phospholamban (at Thr17), and activation of caspase-3. Zacopride treatment (1 μmol/L) was noted to strikingly restore the expression of Kir2.1 and SERCA2 and decrease the activity of CaMKII, phospholamban, and caspase-3. These effects were largely eliminated by co-application of IK1 blocker BaCl2. CaMKII inhibitor KN93 attenuated calcium overload and p-PLB (Thr17) in an IK1-independent manner. IK1-depedent inhibition of CaMKII activity is found to be a key cardiac salvage signaling under Ca2+ dyshomeostasis and reactive oxygen species (ROS) stress. IK1 might be a novel target for pharmacological conditioning of reperfusion arrhythmia, especially for the application after unpredictable ischemia.
Accurate segmentation is an essential task when working with medical images. Recently, deep convolutional neural networks achieved a state-of-the-art performance for many segmentation benchmarks. ...Regardless of the network architecture, the deep learning-based segmentation methods view the segmentation problem as a supervised task that requires a relatively large number of annotated images. Acquiring a large number of annotated medical images is time consuming, and high-quality segmented images (i.e., strong labels) crafted by human experts are expensive. In this paper, we have proposed a method that achieves competitive accuracy from a "weakly annotated" image where the weak annotation is obtained via a 3D bounding box denoting an object of interest. Our method, called "3D-BoxSup," employs a positive-unlabeled learning framework to learn segmentation masks from 3D bounding boxes. Specially, we consider the pixels outside of the bounding box as positively labeled data and the pixels inside the bounding box as unlabeled data. Our method can suppress the negative effects of pixels residing between the true segmentation mask and the 3D bounding box and produce accurate segmentation masks. We applied our method to segment a brain tumor. The experimental results on the BraTS 2017 dataset (Menze et al., 2015; Bakas et al., 2017a,b,c) have demonstrated the effectiveness of our method.
To address the coordinated distribution of motor braking and friction braking for the regenerative braking system, a cooperative braking algorithm based on nonlinear model predictive control (NMPC) ...is proposed, with braking energy recovery power, tire slip rate, and motor torque variation as the optimization objectives, and online optimization of the coordinated distribution of motor braking and friction braking. Using the offline model built in Matlab/Simulink, the cooperative braking algorithm is tested for energy efficiency and braking safety. The results show that when based on World Light Vehicle Test Cycle (WLTC), the energy recovery rate can reach 30.4%, and with a single high braking intensity, the braking safety can still be ensured.
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