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The obtained results show that the proposed technique can be used efficiently to estimate the response of the neural networks and dynamic loads.In this paper, optimized radial basis function neural networks (RBFNNs) are employed to construct a sliding mode control (SMC) strategy for quadrotors with unknown disturbances. VER155008 order At first, the dynamics model of the controlled quadrotor is built, where some unknown external disturbances are considered explicitly. Then SMC is carried out for the position and the attitude control of the quadrotor. However, there are unknown disturbances in the obtained controllers, so RBFNNs are employed to approximate the unknown parts of the controllers. Furtherly, Particle Swarm optimization algorithm (PSO) based on minimizing the absolute approximation errors is used to improve the performance of the controllers. Besides, the convergence of the state tracking errors of the quadrotor is proved. In order to exposit the superiority of the proposed control strategy, some comparisons are made between the RBFNN based SMC with and without PSO. The results show that the strategy with PSO achieves quicker and smoother trajectory tracking, which verifies the effectiveness of the proposed control strategy.Idiopathic Parkinson's Disease (iPD) is a common motor neurodegenerative disorder. It affects more frequently the elderly population, causing a significant emotional burden both for the patient and caregivers, due to the disease-related onset of motor and cognitive disabilities. iPD's clinical hallmark is the onset of cardinal motor symptoms such as bradykinesia, rest tremor, rigidity, and postural instability. However, these symptoms appear when the neurodegenerative process is already in an advanced stage. Furthermore, the greatest challenge is to distinguish iPD from other similar neurodegenerative disorders, "atypical parkinsonisms", such as Multisystem Atrophy, Progressive Supranuclear Palsy and Cortical Basal Degeneration, since they share many phenotypic manifestations, especially in the early stages. The diagnosis of these neurodegenerative motor disorders is essentially clinical. Consequently, the diagnostic accuracy mainly depends on the professional knowledge and experience of the physician. Recent advances in artificial intelligence have made it possible to analyze the large amount of clinical and instrumental information in the medical field. The application machine learning algorithms to the analysis of neuroimaging data appear to be a promising tool for identifying microstructural alterations related to the pathological process in order to explain the onset of symptoms and the spread of the neurodegenerative process. In this context, the search for quantitative biomarkers capable of identifying parkinsonian patients in the prodromal phases of the disease, of correctly distinguishing them from atypical parkinsonisms and of predicting clinical evolution and response to therapy represent the main goal of most current clinical research studies. Our aim was to review the recent literature and describe the current knowledge about the contribution given by machine learning applications to research and clinical management of parkinsonian syndromes.The survival rate of cervical cancer can be improved by the early screening. However, the screening is a heavy task for pathologists. Thus, automatic cervical cell classification model is proposed to assist pathologists in screening. In cervical cell classification, the number of abnormal cells is small, meanwhile, the ratio between the number of abnormal cells and the number of normal cells is small too. In order to deal with the small sample and class imbalance problem, a generative adversarial network (GAN) trained by images of abnormal cells is proposed to obtain the generated images of abnormal cells. Using both generated images and real images, a convolutional neural network (CNN) is trained. We design four experiments, including 1) training the CNN by under-sampled images of normal cells and the real images of abnormal cells, 2) pre-training the CNN by other dataset and fine-tuning it by real images of cells, 3) training the CNN by generated images of abnormal cells and the real images, 4) pre-training the CNN by generated images of abnormal cells and fine-tuning it by real images of cells. Comparing these experimental results, we find that 1) GAN generated images of abnormal cells can effectively solve the problem of small sample and class imbalance in cervical cell classification; 2) CNN model pre-trained by generated images and fine-tuned by real images achieves the best performance whose AUC value is 0.984.Traditional image encryption technology usually encrypts a normal image into a noise matrix, which can protect the image in a certain extent, but noise appearance is easy to arouse the suspicion of attackers. To avoid this problem, a method of encrypting image into carrier image with visual meaning is proposed. Inspired by the existing visually secure encryption technique, we proposed an improved method based on the integer wavelet transform (IWT) and prediction scheme. The secret image is hidden in the high frequency coefficients of IWT to achieve good invisibility, and prediction error are used to replace the pixels of the carrier image to improve the final image quality. Experimental results and analysis show that the quality of the encrypted image is 3.5 dB better than that of the previous ones.Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies worldwide. However, the mechanisms underlying the acquisition of the metastatic potential in PDAC has not been well understood. In this study, we identified a total of 154 genes upregulated in primary tissues of PDAC with liver metastasis using the Genome Cancer Atlas (TCGA) and GSE151580 cohorts. The epithelial-mesenchymal transition and glycolysis were enriched by the liver metastasis-related genes, indicating that the liver metastasis-related genes might be functionally relevant to liver metastasis in PDAC. Moreover, we also found that the liver metastasis-related genes were primarily regulated at epigenetic level. Particularly, SFN, a cell cycle checkpoint protein, and KRT19, a marker gene for ductal cells, were predicted to be regulated by multiple methylation sites at the promoter. Clinically, we for the first time defined a liver metastasis score (LMS), which was derived from liver metastasis-related genes, and closely associated with clinical characteristics such as disease type and tumor grade, in PDAC.