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1.
AbstractBreast cancer is a common malignancy with poor prognosis. Cancer cells are heterogeneous and cancer stem cells (CSCs) are primarily responsible for tumor relapse, treatment-resistance and metastasis, so for breast cancer stem cells (BCSCs). Diets are known to be associated with carcinogenesis. Food-derived polyphenols are able to attenuate the formation and virulence of BCSCs, implying that these compounds and their analogs might be promising agents for preventing breast cancer. In the present review, we summarized the origin and surface markers of BCSCs and possible mechanisms responsible for the inhibitory effects of polyphenols on BCSCs. The suppressive effects of common dietary polyphenols against BCSCs, such as curcumin, epigallocatechin gallate (EGCG) and related polyphenolic compounds were further discussed. 相似文献
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Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%-80%) is used for training and the rest—for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesizing feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesize data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the so-called fairness of machine learning. In this paper, we propose a specific method for synthesizing data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, for example, support vector machines, k-nearest neighbour classifiers deep neural, rule-based classifiers, decision trees, and so forth. The results demonstrated that (a) a significantly more balanced (and fair) classification results can be achieved and (b) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In addition, this approach can be very valuable for the cases when the number of actual available labelled data is small which itself is one of the problems of the contemporary machine learning. 相似文献
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Wenshu Chen Jiajun Gu Yongping Du Fang Song Fanxing Bu Jinghan Li Yang Yuan Ruichun Luo Qinglei Liu Di Zhang 《Advanced functional materials》2020,30(25)
Large‐scale production of hydrogen from water‐alkali electrolyzers is impeded by the sluggish kinetics of hydrogen evolution reaction (HER) electrocatalysts. The hybridization of an acid‐active HER catalyst with a cocatalyst at the nanoscale helps boost HER kinetics in alkaline media. Here, it is demonstrated that 1T–MoS2 nanosheet edges (instead of basal planes) decorated by metal hydroxides form highly active / heterostructures, which significantly enhance HER performance in alkaline media. Featured with rich / sites, the fabricated 1T–MoS2 QS/Ni(OH)2 hybrid (quantum sized 1T–MoS2 sheets decorated with Ni(OH)2 via interface engineering) only requires overpotentials of 57 and 112 mV to drive HER current densities of 10 and 100 mA cm?2, respectively, and has a low Tafel slope of 30 mV dec?1 in 1 m KOH. So far, this is the best performance for MoS2‐based electrocatalysts and the 1T–MoS2 QS/Ni(OH)2 hybrid is among the best‐performing non‐Pt alkaline HER electrocatalysts known. The HER process is durable for 100 h at current densities up to 500 mA cm?2. This work not only provides an active, cost‐effective, and robust alkaline HER electrocatalyst, but also demonstrates a design strategy for preparing high‐performance catalysts based on edge‐rich 2D quantum sheets for other catalytic reactions. 相似文献
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As the development of cloud computing and the convenience of wireless sensor netowrks, smart devices are widely used in daily life, but the security issues of the smart devices have not been well resolved. In this paper, we present a new NTRU-type public-key cryptosystem over the binary field. Specifically, the security of our scheme relies on the computational intractability of an unbalanced sparse polynomial ratio problem (DUSPR). Through theoretical analysis, we prove the correctness of our proposed cryptosystem. Furthermore, we implement our scheme using the NTL library, and conduct a group of experiments to evaluate the capabilities and consuming time of encryption and decryption. Our experiments result demonstrates that the NTRU-type public-key cryptosystem over the binary field is relatively practical and effective. 相似文献
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The application of deep learning in the field of object detection has
experienced much progress. However, due to the domain shift problem, applying an
off-the-shelf detector to another domain leads to a significant performance drop. A
large number of ground truth labels are required when using another domain to train
models, demanding a large amount of human and financial resources. In order to avoid
excessive resource requirements and performance drop caused by domain shift, this
paper proposes a new domain adaptive approach to cross-domain vehicle detection. Our
approach improves the cross-domain vehicle detection model from image space and
feature space. We employ objectives of the generative adversarial network and cycle
consistency loss for image style transfer in image space. For feature space, we align
feature distributions between the source domain and the target domain to improve the
detection accuracy. Experiments are carried out using the method with two different
datasets, proving that this technique effectively improves the accuracy of vehicle
detection in the target domain. 相似文献