Cancer is the second leading cause of death worldwide, and the death rate of lung cancer is much higher than other types of cancers. In recent years, numerous novel computer-aided diagnostic ...techniques with deep learning have been designed to detect lung cancer in early stages. However, deep learning models are easy to overfit, and the overfitting problem always causes lower performance. To solve this problem of lung cancer classification tasks, we proposed a hybrid framework called LCGANT. Specifically, our framework contains two main parts. The first part is a lung cancer deep convolutional GAN (LCGAN) to generate synthetic lung cancer images. The second part is a regularization enhanced transfer learning model called VGG-DF to classify lung cancer images into three classes. Our framework achieves a result of 99.84% ± 0.156% (accuracy), 99.84% ± 0.153% (precision), 99.84% ± 0.156% (sensitivity), and 99.84% ± 0.156% (F1-score). The result reaches the highest performance of the dataset for the lung cancer classification task. The proposed framework resolves the overfitting problem for lung cancer classification tasks, and it achieves better performance than other state-of-the-art methods.
(Background) We proposed a novel computer-aided diagnosis (CAD) system based on the hybridization of biogeography-based optimization (BBO) and particle swarm optimization (PSO), with the goal of ...detecting pathological brains in MRI scanning. (Method) The proposed method used wavelet entropy (WE) to extract features from MR brain images, followed by feed- forward neural network (FNN) with training method of a Hybridization of BBO and PSO (HBP), which combined the exploration ability of BBO and exploitation ability of PSO. (Results) The 10 repetition of k-fold cross validation result showed that the proposed HBP outperformed existing FNN training methods and that the proposed WE + HBP-FNN outperformed fourteen state-of-the-art CAD systems of MR brain classification in terms of classification accuracy. The proposed method achieved accuracy of 100%, 100%, and 99.49% over Dataset-66, Dataset-160, and Dataset-255, respectively. The offline learning cost 208.2510 s for Dataset-255, and merely 0.053s for online prediction. (Conclusion) The proposed WE + HBP-FNN method achieves nearly perfect detection pathological brains in MRI scanning.
Generating an yttrium concentration-gradient LiYO2-Y2O3 coating layer on surface of LiNiO2 particles improves the interface stability and cycling performance of Ni-rich cathode materials.
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Lithium nickel oxide (LiNiO2) cathode materials are featured with high capacity and low cost for rechargeable lithium-ion batteries but suffer from severe interface and structure instability. Here we report that rationally designed LiNiO2 via concentration-gradient yttrium modification exhibits alleviative side reactions and improved electrochemical performance. The LiNiO2 cathode with LiYO2-Y2O3 coating layer delivers a discharge capacity of 225 mAh g−1 with a high initial Coulombic efficiency of 93.4%. These improvements can be attributed to the formation of in-situ modified hybrid LiYO2-Y2O3 coating layer, which suppresses phase transformation, electrolyte oxidation and salt dissociation due to the formation of protective cathode electrolyte interface. The results indicate promising application of concentration-gradient yttrium coating as a facile approach to stabilize nickel-rich cathode materials.
Abstract Controversies exist whether season of birth is associated with schizophrenia development later in life, and evidence has mainly come from studies done in developed countries. This study ...examines the association between season of birth and risk for schizophrenia in China, with special attention to geographical region, urbanity, and gender. Using data from China’s Second National Sampling Survey on Disability, a large-scale, nationally representative sample (N=2,052,694), this study employs discrete-time hazard models to compare the risk for schizophrenia development for people born in different seasons, and conducts subsample analyses by geographical region, urbanity, and gender. People born in the spring have the highest risk when compared to people born in the winter, summer or autumn. Furthermore, the relatively higher risk for people born in the spring is greater in the southern half of the country, in rural areas, and for women. The findings are consistent with results from a robustness check done among people who were conceived and born from 1955 to 1965, periods before, during, and after the 1959-1961 Chinese Famine. This study supports the presence of an association between season of birth and risk for schizophrenia development and of heterogeneity by geographical region, urbanity, and gender.
Increased activity of the epigenetic modifier EZH2 has been associated with different cancers. However, evidence for a functional role of EZH2 in tumorigenesis in vivo remains poor, in particular in ...metastasizing solid cancers. Here we reveal central roles of EZH2 in promoting growth and metastasis of cutaneous melanoma. In a melanoma mouse model, conditional Ezh2 ablation as much as treatment with the preclinical EZH2 inhibitor GSK503 stabilizes the disease through inhibition of growth and virtually abolishes metastases formation without affecting normal melanocyte biology. Comparably, in human melanoma cells, EZH2 inactivation impairs proliferation and invasiveness, accompanied by re-expression of tumour suppressors connected to increased patient survival. These EZH2 target genes suppress either melanoma growth or metastasis in vivo, revealing the dual function of EZH2 in promoting tumour progression. Thus, EZH2-mediated epigenetic repression is highly relevant especially during advanced melanoma progression, which makes EZH2 a promising target for novel melanoma therapies.
Object tracking task can be divided into two subtasks: classification and regression. Some state-of-the-art methods utilize classification score and quality estimation score to select proposal box. ...However, their classification branches and quality estimation branches are inconsistent in the training stage and the inference stage. Besides, the existing anchor-based regression relies on a lot of prior knowledge, which aggravates the burden of trackers. To alleviate these problems, we propose a simple and effective Siamese offset-aware object tracking (SiamOA) method. More specifically, we firstly propose a IoU-guided classification branch which unifies original classification branch and regression quality estimation branch and use intersection over union (IoU) to guide three classification branches to eliminate the inconsistency between training and inference. We secondly propose a more accurate offset-aware regression branch, by coarsely estimating the interval into which the bounding box edge offsets fall, and accurately predicting the displacements of the offsets within this interval. To optimize the classification and regression branches in an end-to-end manner, we thirdly propose a joint classification and regression alternative refining strategy to introduce the information exchange between them. We conduct extensive experiments on some challenging benchmarks like VOT2016, VOT2018, OTB100, UAV123, GOT-10 k, and results show the excellent performance of our SiamOA.
Working memory (WM) - one of the brain ability that maintains information - can evaluate the function of brain. Activities related to memory sustention, inhibition and disinhibition have gathered ...significant attention for the basic neurocognitive architecture. Although researchers have proposed some brain models that attempt to explain the entire procedure of WM, little evidence can proof and describe it, and more particularly, regions and structures of maintenance, inhibition and disinhibition require more investigation. We used phase lock coherence and general partial directed coherence to construct connections among four adaptively fitted EEG sources, and we also applied previous published models to describe the brain circuits of maintenance, inhibition and disinhibition. Referring to a classical visual n-back paradigm, we recruited forty five mental health undergraduates in this experiment. We found that the bilateral prefrontal cortex (PFC) mainly focused on some cognitive components, for example, rehearsal before recognition to classify objects, inhibition to maintain positive memory and activities, and disinhibition to arouse or activate subsequent interactions in brain. Meanwhile, the right PFC sometimes could assist left PFC to implement high capacity WM tasks. By contrast, the posterior regions, PPC, tends to be engaged in attention arousing and maintaining. These two findings suggest that a) the recurrent maintenance circuit may keep the brain executing positive cognitive components, b) then the instantly monitoring inhibition would pause the deadlocked sustention function to save energy, and c) the arriving of disinhibition arouses the next step in brain to select new subject or focus on novel subjects.
The deep learning model has demonstrated excellent performance in the fitting of data and knowledge. For hyperspectral images, accurate classification is still difficult in the case of limited ...samples and high-dimensional relevance. In this paper, we propose a collaborative optimization parallel convolution network consisting of 3D-2D CNN for hyperspectral image classification. One branch of the parallel network is a 3D-CNN consisting of three blocks for extracting spectrum features and spectrum correlation. The three blocks include a 3D bottleneck block (convolution), SEblock (attention), and a spatial-spectrum convolution module. Secondly, the diverse Region feature extraction network is employed as a spatial-spectrum feature computing module. Finally, the classification predictions from the two branches are fused to obtain the classification results. By comparing the experimental results conducted on three datasets, the proposed method performs significantly better than the SOTA methods in comparison and has better generalization capability.
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