Highlights d Four methods, PCA, MDS, t-SNE, and UMAP, are evaluated on 71 bulk transcriptomic datasets d UMAP is overall superior to PCA and MDS and shows some advantages over t-SNE d UMAP can efficiently and effectively reveal clusters in twodimensional space d Clusters revealed by UMAP are associated with biological features and clinical traits

Targeting the medical monitoring applications of wireless body area networks (WBANs), a hybrid medium access control protocol using an interrupt mechanism (I-MAC) is proposed to improve the energy and time slot utilization efficiency and to meet the data delivery delay requirement at the same time. Unlike existing hybrid MAC protocols, a superframe structure with a longer length is adopted to avoid unnecessary beacons. The time slots are mostly allocated to nodes with periodic data sources. Short interruption slots are inserted into the superframe to convey the urgent data and to guarantee the real-time requirements of these data. During these interruption slots, the coordinator can break the running superframe and start a new superframe. A contention access period (CAP) is only activated when there are more data that need to be delivered. Experimental results show the effectiveness of the proposed MAC protocol in WBANs with low urgent traffic.

Combining medical data and machine learning has fully utilized the value of medical data. However, medical data contain a large amount of sensitive information, and the inappropriate handling of data can lead to the leakage of personal privacy. Thus, both publishing data and training data in machine learning may reveal the privacy of patients. To address the above issue, we propose two effective approaches. One combines a differential privacy and decision tree (DPDT) approach to provide strong privacy guarantees for publishing data, which establishes a weight calculation system based on the classification and regression tree (CART) method and takes weights as a new element of differential privacy to participate in privacy protection and reduce the negative impact of differential privacy on data availability. Another uses the differentially private mini-batch gradient descent algorithm (DPMB) to provide strong protection for training data; it tracks the privacy loss and allows the model to satisfy differential privacy in the process of gradient descent to prevent attackers from invading personal privacy with the training data. It is worth mentioning that, in this paper, we adopt the data processed by DPDT as the training data of DPMB to further strengthen the privacy of data. INDEX TERMS Deep learning, data privacy, differential privacy, data publishing.

Fractional complex chaotic systems have attracted great interest recently. However, most of scholars adopted integer real chaotic system and fractional real and integer complex chaotic systems to improve the security of communication. In this paper, the advantages of fractional complex chaotic synchronization (FCCS) in secure communication are firstly demonstrated. To begin with, we propose the definition of fractional difference function synchronization (FDFS) according to difference function synchronization (DFS) of integer complex chaotic systems. FDFS makes communication secure based on FCCS possible. Then we design corresponding controller and present a general communication scheme based on FDFS. Finally, we respectively accomplish simulations which transmit analog signal, digital signal, voice signal, and image signal. Especially for image signal, we give a novel image cryptosystem based on FDFS. The results demonstrate the superiority and good performances of FDFS in secure communication.