Tumor metastasis remains the main cause of breast cancer-related deaths, especially delayed breast cancer distant metastasis. The current study assessed the frequency of CD44
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/CD24
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breast cancer ...cells in 576 tissue specimens for associations with clinicopathological features and metastasis and investigated the underlying molecular mechanisms. The results indicated that higher frequency (≥19.5%) of CD44
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/CD24
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cells was associated with delayed postoperative breast cancer metastasis. Furthermore, CD44
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/CD24
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triple negative breast cancer (TNBC) cells spontaneously converted into CD44
+
/CD24
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cancer stem cells (CSCs) with properties similar to CD44
+
/CD24
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CSCs from primary human breast cancer cells and parental TNBC cells in terms of stemness marker expression, self-renewal, differentiation, tumorigenicity, and lung metastasis in vitro and
in NOD/SCID mice
. RNA sequencing identified several differentially expressed genes (DEGs) in newly converted CSCs and
RHBDL2
, one of the DEGs, expression was upregulated. More importantly,
RHBDL2
silencing inhibited the YAP1/USP31/NF-κB signaling and attenuated spontaneous CD44
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/CD24
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cell conversion into CSCs and their mammosphere formation. These findings suggest that the frequency of CD44
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/CD24
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tumor cells and
RHBDL2
may be valuable for prognosis of delayed breast cancer metastasis, particularly for TNBC.
Creating unique nanostructures is imperative to develop new generation of electrodes with high electro-capacitive property. Herein the molybdenum disulfide/polyaniline (MoS2/PANI) composites with ...different PANI loadings (40, 53, and 70 wt%) were prepared using MoS2 nanosheet as the substrate. At a 53 wt% loading amount of PANI, the obtained MoS2/PANI-53 nanopallet electrode material displayed the highest electro-capacitive property. The rational combination of the two components allowed the PANI thin layer to uniformly cover the surface of the MoS2 nanosheet and the synergistic effect of the high specific capacitance of PANI along with the fast ionic conductivity and mechanical stability of MoS2 make MoS2/PANI-53 as a suitable electrode material for supercapacitors. Therefore the MoS2/PANI-53 nanopallet symmetric supercapacitor delivered high energy density (35 Wh kg−1 at the power density of 335 W kg−1) and excellent cycling stability (81% capacitance retention after 8000 cycles). This work could be give rise to conducting polymer based composite electrode material with high performance for functional supercapacitor devices.
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•The MoS2/PANI-53 electrode material with a pallet-like morphology was prepared.•The MoS2/PANI-53 electrode displays high specific capacitance and excellent rate capability.•The MoS2/PANI-53 based symmetric supercapacitor delivers high energy density and good cycling stability.
In multi-task learning, multiple interrelated tasks are jointly learned to achieve better performance. In many cases, if we can identify which tasks are related, we can also clearly identify which ...tasks are unrelated. In the past, most researchers emphasized exploiting correlations among interrelated tasks while completely ignoring the unrelated tasks that may provide valuable prior knowledge for multi-task learning. In this paper, a new approach is developed to hierarchically learn a tree of multi-task metrics by leveraging prior knowledge about both the related tasks and unrelated tasks. First, a visual tree is constructed to hierarchically organize large numbers of image categories in a coarse-to-fine fashion. Over the visual tree, a multi-task metric classifier is learned for each node by exploiting both the related and unrelated tasks, where the learning tasks for training the classifiers for the sibling child nodes under the same parent node are treated as the interrelated tasks, and the others are treated as the unrelated tasks. In addition, the node-specific metric for the parent node is propagated to its sibling child nodes to control inter-level error propagation. Our experimental results demonstrate that our hierarchical metric learning algorithm achieves better results than other state-of-the-art algorithms.
In this paper, the equations of motion for modeling a spacecraft near an earth-threatening asteroid are proposed and studied for deflecting the asteroid using a gravity tractor that effectively tows ...the asteroid with mutual gravity attraction. The motion of the spacecraft is expressed by separation distance between the two objects, two Eulerian angles expressing the attitude of the spacecraft–asteroid system, and their derivatives; it is assumed that the spacecraft and asteroid are connected with a variable-length gravity tether. The motion of the spacecraft in an artificial halo orbit (AHO) is investigated using the proposed equations. These equations are suitable for the investigation because the spacecraft in the AHO moves in a circular orbit at a constant angular velocity and distance from the asteroid. The results indicate that the AHO is unstable, and a linear quadratic regulator will be required to control the spacecraft. Numerical simulation results suggest that the AHO–AHO transfer can be realized by employing a control law that linearizes the proposed equations. The achievable deflection distance yielded by 1000 days of a towing mission is calculated for fictional Earth impactors; the results indicate that the proposed system is feasible for conducting long-term simulations.
•Equations of motion are proposed to model spacecraft near earth-threatening asteroids.•Asteroid is deflected with a gravity tractor.•Proposed equations are suitable for spacecraft in AHO.•AHO–AHO transfer can be realized with a control law.•Proposed system is feasible for long-term simulations.
Visual reranking has been widely deployed to refine the traditional text-based image retrieval. Its current trend is to combine the retrieval results from various visual features to boost reranking ...precision and scalability. And its prominent challenge is how to effectively exploit the complementary property of different features. Another significant issue raises from the noisy instances, from manual or automatic labels, which makes the exploration of such complementary property difficult. This paper proposes a novel image reranking by introducing a new Co-Regularized Multi- Graph Learning (Co-RMGL) framework, in which intra-graph and inter-graph constraints are integrated to simultaneously encode the similarity in a single graph and the consistency across multiple graphs. To deal with the noisy instances, weakly supervised learning via co-occurred visual attribute is utilized to select a set of graph anchors to guide multiple graphs alignment and fusion, and to filter out those pseudo labeling instances to highlight the strength of individual features. After that, a learned edge weighting matrix from a fused graph is used to reorder the retrieval results. We evaluate our approach on four popular image retrieval datasets and demonstrate a significant improvement over state-of-the-art methods.
In this article, a discriminative fast hierarchical learning algorithm is developed for supporting multiclass image classification, where a visual tree is seamlessly integrated with multitask ...learning to achieve fast training of the tree classifier hierarchically (i.e., a set of structural node classifiers over the visual tree). By partitioning a large number of categories hierarchically in a coarse-to-fine fashion, a visual tree is first constructed and further used to handle data imbalance and identify the interrelated learning tasks automatically (e.g., the tasks for learning the node classifiers for the sibling child nodes under the same parent node are strongly interrelated), and a multitask SVM classifier is trained for each nonleaf node to achieve more effective separation of its sibling child nodes at the next level of the visual tree. Both the internode visual similarities and the interlevel visual correlations are utilized to train more discriminative multitask SVM classifiers and control the interlevel error propagation effectively, and a stochastic gradient descent (SGD) algorithm is developed for learning such multitask SVM classifiers with higher efficiency. Our experimental results have demonstrated that our fast hierarchical learning algorithm can achieve very competitive results on both the classification accuracy rates and the computational efficiency.
Different types of surface defects will occur during the production of strip steel. To ensure production quality, it is essential to classify these defects. Our research indicates that two main ...problems exist in the existing strip steel surface defect classification methods: (1) they cannot solve the problem of unbalanced data using few-shot in reality, (2) they cannot meet the requirement of online real-time classification. To solve the aforementioned problems, a relational knowledge distillation self-adaptive residual shrinkage network (RKD-SARSN) is presented in this work. First, the data enhancement strategy of Cycle GAN defective sample migration is designed. Second, the self-adaptive residual shrinkage network (SARSN) is intended as the backbone network for feature extraction. An adaptive loss function based on accuracy and geometric mean (Gmean) is proposed to solve the problem of unbalanced samples. Finally, a relational knowledge distillation model (RKD) is proposed, and the functions of GUI operation interface encapsulation are designed by combining image processing technology. SARSN is used as a teacher model, its generalization performance is transferred to the lightweight network ResNet34, and it is conveniently deployed as a student model. The results show that the proposed method can improve the deployment efficiency of the model and ensure the real-time performance of the classification algorithms. It is superior to other mainstream algorithms for fine-grained images with unbalanced data classification.
Aerial object detection, as object detection in aerial images captured from an overhead perspective, has been widely applied in urban management, industrial inspection, and other aspects. However, ...the performance of existing aerial object detection algorithms is hindered by variations in object scales and orientations attributed to the aerial perspective. This survey presents a comprehensive review of recent advances in aerial object detection. We start with some basic concepts of aerial object detection and then summarize the five imbalance problems of aerial object detection, including scale imbalance, spatial imbalance, objective imbalance, semantic imbalance, and class imbalance. Moreover, we classify and analyze relevant methods and especially introduce the applications of aerial object detection in practical scenarios. Finally, the performance evaluation is presented on two popular aerial object detection datasets VisDrone-DET and DOTA, and we discuss several future directions that could facilitate the development of aerial object detection.
In this work, high-speed 850 nm GaAs/AlGaAs based dual-depletion-region photodetectors are demonstrated, which aim to meet the increasing need for bandwidth scaling and data rate in short-reach ...optical links. The devices exhibit the low dark current of ∼134.5 fA and responsivity of ∼0.44 A/W at 850 nm. The 3-dB bandwidth of 20 μm and 28 μm diameter devices with 50-ohm characteristic impedance transmission line were measured to be 29.72 GHz and 24.71 GHz respectively. While by incorporating inductive peaking via high characteristic impedance transmission line, the 20 μm and 28 μm diameter devices achieved maximum 3-dB bandwidth of 46.06 GHz and 36.09 GHz respectively. To the best of our knowledge, this 20 μm diameter device exhibits the highest 3-dB bandwidth among all the 850 nm photodetectors. This research showcases the potential of 850 nm dual-depletion-region photodetectors as promising solutions for high-speed short-reach optical communication systems, offering improved performance in terms of bandwidth and enabling the advancement of data transmission capabilities.
In this study, MOF/BiFeO₃ composite (MOF, metal-organic framework) has been synthesized successfully through a one-pot hydrothermal method. The MOF/BiFeO₃ composite samples, pure MOF samples and ...BiFeO₃ samples were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and by UV-vis spectrophotometry. The results and analysis reveal that MOF/BiFeO₃ composite has better photocatalytic behavior for methylene blue (MB) compared to pure MOF and pure BiFeO₃. The enhancement of photocatalytic performance should be due to the introduction of MOF change the surface morphology of BiFeO
which will increase the contact area with MB. This composing strategy of MOF/BiFeO₃ composite may bring new insight into the designing of highly efficient photocatalysts.
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