Purpose
Wireless capsule endoscopy (WCE) enables physicians to examine the digestive tract without any surgical operations, at the cost of a large volume of images to be analyzed. In the ...computer‐aided diagnosis of WCE images, the main challenge arises from the difficulty of robust characterization of images. This study aims to provide discriminative description of WCE images and assist physicians to recognize polyp images automatically.
Methods
We propose a novel deep feature learning method, named stacked sparse autoencoder with image manifold constraint (SSAEIM), to recognize polyps in the WCE images. Our SSAEIM differs from the traditional sparse autoencoder (SAE) by introducing an image manifold constraint, which is constructed by a nearest neighbor graph and represents intrinsic structures of images. The image manifold constraint enforces that images within the same category share similar learned features and images in different categories should be kept far away. Thus, the learned features preserve large intervariances and small intravariances among images.
Results
The average overall recognition accuracy (ORA) of our method for WCE images is 98.00%. The accuracies for polyps, bubbles, turbid images, and clear images are 98.00%, 99.50%, 99.00%, and 95.50%, respectively. Moreover, the comparison results show that our SSAEIM outperforms existing polyp recognition methods with relative higher ORA.
Conclusion
The comprehensive results have demonstrated that the proposed SSAEIM can provide descriptive characterization for WCE images and recognize polyps in a WCE video accurately. This method could be further utilized in the clinical trials to help physicians from the tedious image reading work.
The paper at hand studies the heat engine provided by black holes in the presence of massive gravity. The main motivation is to investigate the effects of massive gravity on different properties of ...the heat engine. It will be shown that massive gravity parameters modify the efficiency of engine on a significant level. Furthermore, it will be pointed out that it is possible to have a heat engine for non-spherical black holes in massive gravity, and therefore, we will study the effects of horizon topology on the properties of heat engine. Surprisingly, it will be shown that the highest efficiency for the heat engine belongs to black holes with the hyperbolic horizon, while the lowest one belongs to the spherical black holes.
Preliminary studies showed that miR-21 is overexpressed in some human cancers. However, the role of miR-21 in cancer is still unclear and even controversial. Our purpose was to investigate the ...biological effects of miR-21 on A549 non-small cell lung cancer (NSCLC) cells and the underlying mechanisms of those effects. The expression of miR-21 was quantified in serum samples from patients with NSCLC. A549 cells were transfected with miR-NC-sponge or miR-21-sponge only, or with miR-21-sponge plus PDCD4 small-interfering RNA (siRNA). The expression of miR-21 and PDCD4 mRNA in transfected cells was quantified by real-time polymerase chain reaction and the expression of PDCD4 protein by Western blot. Cell proliferation, apoptosis, migration, and invasion assays were performed to determine the biological effects of miR-21 expression and PDCD4 inhibition. miR-21 was overexpressed in serum from patients with NSCLC. Reduced miR-21 expression was observed following transfection with miR-21-sponge in A549 NSCLC cells. Co-transfection of miR-21-sponge with PDCD4 siRNA upregulated miR-21 expression in these cells. PDCD4 mRNA and protein levels were increased 2.14-fold and 2.16-fold, respectively, following inhibition of miR-21 expression. Inhibition of miR-21 expression following transfection of miR-21-sponge reduced cell proliferation, migration, and invasion of A549 cells. Depletion of PDCD4 by siRNA transfection reversed the reduction of cell proliferation, migration, and invasion induced by inhibition of miR-21 in A549 cells. It indicates that miR-21 is highly expressed in patients with NSCLC and inhibition of miR-21 expression reduces proliferation, migration, and invasion of A549 cells by upregulating PDCD4 expression. Modulation of miR-21 or PDCD4 expression may provide a potentially novel therapeutic approach for NSCLC.
Increasing detection of unruptured intracranial aneurysms, catastrophic outcomes from subarachnoid hemorrhage, and risks and cost of treatment necessitate defining objective predictive parameters of ...aneurysm rupture risk. Image-based computational fluid dynamics models have suggested associations between hemodynamics and intracranial aneurysm rupture, albeit with conflicting findings regarding wall shear stress. We propose that the "high-versus-low wall shear stress" controversy is a manifestation of the complexity of aneurysm pathophysiology, and both high and low wall shear stress can drive intracranial aneurysm growth and rupture. Low wall shear stress and high oscillatory shear index trigger an inflammatory-cell-mediated pathway, which could be associated with the growth and rupture of large, atherosclerotic aneurysm phenotypes, while high wall shear stress combined with a positive wall shear stress gradient trigger a mural-cell-mediated pathway, which could be associated with the growth and rupture of small or secondary bleb aneurysm phenotypes. This hypothesis correlates disparate intracranial aneurysm pathophysiology with the results of computational fluid dynamics in search of more reliable risk predictors.
Rapidly random-exploring tree (RRT) and its variants are very popular due to their ability to quickly and efficiently explore the state space. However, they suffer sensitivity to the initial solution ...and slow convergence to the optimal solution, which means that they consume a lot of memory and time to find the optimal path. It is critical to quickly find a short path in many applications such as the autonomous vehicle with limited power/fuel. To overcome these limitations, we propose a novel optimal path planning algorithm based on the convolutional neural network (CNN), namely the neural RRT* (NRRT*). The NRRT* utilizes a nonuniform sampling distribution generated from a CNN model. The model is trained using quantities of successful path planning cases. In this article, we use the A* algorithm to generate the training data set consisting of the map information and the optimal path. For a given task, the proposed CNN model can predict the probability distribution of the optimal path on the map, which is used to guide the sampling process. The time cost and memory usage of the planned path are selected as the metric to demonstrate the effectiveness and efficiency of the NRRT*. The simulation results reveal that the NRRT* can achieve convincing performance compared with the state-of-the-art path planning algorithms. Note to Practitioners -The motivation of this article stems from the need to develop a fast and efficient path planning algorithm for practical applications such as autonomous driving, warehouse robot, and countless others. Sampling-based algorithms are widely used in these areas due to their good scalability and high efficiency. However, the quality of the initial path is not guaranteed and it takes much time to converge to the optimal path. To quickly obtain a high-quality initial path and accelerate the convergence speed, we propose the NRRT*. It utilizes a nonuniform sampling distribution and achieves better performance. The NRRT* can be also applied to other sampling-based algorithms for improved results in different applications.
A relative humidity sensor based on a graphene oxide-coated few-mode fiber Mach-Zehnder interferometer (MZI) is proposed in this paper. The MZI was made by splicing a segment of the few-mode fiber ...(FMF) between two segments of a no-core fiber (NCF) and two segments of a single mode fiber (SMF) located outside the two NCFs. The core and cladding of the FMF acted as interferometric arms, while the NCFs acted as couplers for splitting and recombining light due to mismatch of mode field diameter. The cladding of the FMF was corroded with hydrofluoric acid, and a layer of graphene oxide (GO) film was coated on the corroded cladding of FMF via the natural deposition method. The refractive index of GO varied upon absorption the water molecules. As a result, the phase difference of the MZI varied and the wavelength of the resonant dip shifted with a change in the ambient relative humidity (RH). High humidity sensitivity of 0.191 and 0.061 nm/%RH in the RH range of 30-55% and 55-95%, respectively, were achieved experimentally. The high sensitivity, compact size, and simple manufacturing of the proposed sensor could offer attractive applications in fields of chemical sensors and biochemical detection.
Wireless capsule endoscopy (WCE) enables noninvasive and painless direct visual inspection of a patient's whole digestive tract, but at the price of long time reviewing large amount of images by ...clinicians. Thus, an automatic computer-aided technique to reduce the burden of physicians is highly demanded. In this paper, we propose a novel color feature extraction method to discriminate the bleeding frames from the normal ones, with further localization of the bleeding regions. Our proposal is based on a twofold system. First, we make full use of the color information of WCE images and utilize K-means clustering method on the pixel represented images to obtain the cluster centers, with which we characterize WCE images as words-based color histograms. Then, we judge the status of a WCE frame by applying the support vector machine (SVM) and K-nearest neighbor methods. Comprehensive experimental results reveal that the best classification performance is obtained with YCbCr color space, cluster number 80 and the SVM. The achieved classification performance reaches 95.75% in accuracy, 0.9771 for AUC, validating that the proposed scheme provides an exciting performance for bleeding classification. Second, we propose a two-stage saliency map extraction method to highlight bleeding regions, where the first-stage saliency map is created by means of different color channels mixer and the second-stage saliency map is obtained from the visual contrast. Followed by an appropriate fusion strategy and threshold, we localize the bleeding areas. Quantitative as well as qualitative results show that our methods could differentiate the bleeding areas from neighborhoods correctly.
Multiple robots collaboratively achieve a common coverage goal efficiently, which can improve work capacity, share coverage tasks, and reduce completion time. In this paper, a neural dynamics (ND) ...approach is proposed for complete area coverage navigation by multiple robots. A bioinspired neural network (NN) is designed to model the workspace and guide a swarm of robots for the coverage mission. The dynamics of each neuron in the topologically organized NN is characterized by an ND equation. Each mobile robot regards other robots as moving obstacles. Each robot path is autonomously generated from the neural activity landscape of the NN and the previous robot position. The proposed model algorithm is computationally efficient. The feasibility is validated by simulation, comparison studies, and experiments.
Magnetic localization methods have been widely studied to provide accurate position and orientation information of intra-body objects, such as wireless capsule endoscopes. However, these methods ...cannot work well in dynamic scenarios when using a wearable sensor array due to the geomagnetic field. In this article, we propose a novel approach for tracking wireless capsules in a mobile setup based on differential signals from adjacent magnetic sensors. Compared to the previous passive magnetic localization framework, the proposed algorithm can eliminate the interference of the geomagnetic field without introducing other compensation sensors or signals. As a result, the proposed method can be used for wearable sensor arrays without knowing the distribution of the geomagnetic field and the movement of the sensor array in advance. Moreover, the relationship between position error and distance of adjacent sensors has been studied to improve tracking accuracy. Finally, a sensor array with 16 three-axis magnetic sensors is developed. Both static and dynamic experiments have been carried out, whose results verified the effectiveness of the proposed method.
Image-based computational fluid dynamics holds a prominent position in the evaluation of intracranial aneurysms, especially as a promising tool to stratify rupture risk. Current computational fluid ...dynamics findings correlating both high and low wall shear stress with intracranial aneurysm growth and rupture puzzle researchers and clinicians alike. These conflicting findings may stem from inconsistent parameter definitions, small datasets, and intrinsic complexities in intracranial aneurysm growth and rupture. In Part 1 of this 2-part review, we proposed a unifying hypothesis: both high and low wall shear stress drive intracranial aneurysm growth and rupture through mural cell-mediated and inflammatory cell-mediated destructive remodeling pathways, respectively. In the present report, Part 2, we delineate different wall shear stress parameter definitions and survey recent computational fluid dynamics studies, in light of this mechanistic heterogeneity. In the future, we expect that larger datasets, better analyses, and increased understanding of hemodynamic-biologic mechanisms will lead to more accurate predictive models for intracranial aneurysm risk assessment from computational fluid dynamics.
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