In this paper, the task of cross-network node classification, which leverages the abundant labeled nodes from a source network to help classify unlabeled nodes in a target network, is studied. The existing domain adaptation algorithms generally fail to model the network structural information, and the current network embedding models mainly focus on single-network applications. Thus, both of them cannot be directly applied to solve the cross-network node classification problem. This motivates us to propose an adversarial cross-network deep network embedding (ACDNE) model to integrate adversarial domain adaptation with deep network embedding so as to learn network-invariant node representations that can also well preserve the network structural information. In ACDNE, the deep network embedding module utilizes two feature extractors to jointly preserve attributed affinity and topological proximities between nodes. In addition, a node classifier is incorporated to make node representations label-discriminative. Moreover, an adversarial domain adaptation technique is employed to make node representations network-invariant. Extensive experimental results demonstrate that the proposed ACDNE model achieves the state-of-the-art performance in cross-network node classification.

The race for the next generation of painless and reliable glucose monitoring for diabetes mellitus is on. As technology advances, both diagnostic techniques and equipment improve. This review describes the main technologies currently being explored for noninvasive glucose monitoring. The principle of each technology is mentioned; its advantages and limitations are then discussed. The general description and the corresponding results for each device are illustrated, as well as the current status of the device and the manufacturer; internet references for the devices are listed where appropriate. Ten technologies and eleven potential devices are included in this review. Near infrared spectroscopy has become a promising technology, among others, for blood glucose monitoring. Although some reviews have been published already, the rapid development of technologies and information makes constant updating mandatory. While advances have been made, the reliability and the calibration of noninvasive instruments could still be improved, and more studies carried out under different physiological conditions of metabolism, bodily fluid circulation, and blood components are needed.

RNA-binding protein (RBP) is a class of proteins that bind to and accompany RNAs in regulating biological processes. An RBP may have multiple target RNAs, and its aberrant expression can cause multiple diseases. Methods have been designed to predict whether a specific RBP can bind to an RNA and the position of the binding site using binary classification model. However, most of the existing methods do not take into account the binding similarity and correlation between different RBPs. While methods employing multiple labels and Long Short Term Memory Network (LSTM) are proposed to consider binding similarity between different RBPs, the accuracy remains low due to insufficient feature learning and multi-label learning on RNA sequences. In response to this challenge, the concept of RNA-RBP Binding Network (RRBN) is proposed in this paper to provide theoretical support for multi-label learning to identify RBPs that can bind to RNAs. It is experimentally shown that the RRBN information can significantly improve the prediction of unknown RNA−RBP interactions. To further improve the prediction accuracy, we present the novel computational method iDeepMV which integrates multi-view deep learning technology under the multi-label learning framework. iDeepMV first extracts data from the views of amino acid sequence and dipeptide component based on the RNA sequences as the original view. Deep neural network models are then designed for the respective views to perform deep feature learning. The extracted deep features are fed into multi-label classifiers which are trained with the RNA−RBP interaction information for the three views. Finally, a voting mechanism is designed to make comprehensive decision on the results of the multi-label classifiers. Our experimental results show that the prediction performance of iDeepMV, which combines multi-view deep feature learning models with RNA−RBP interaction information, is significantly better than that of the state-of-the-art methods. iDeepMV is freely available at http://www.csbio.sjtu.edu.cn/bioinf/iDeepMV for academic use. The code is freely available at http://github.com/uchihayht/iDeepMV.

BackgroundPatients who develop acute stroke are at high risk for deterioration in the first 48–72 hours after admission. An effective educational intervention is needed.ObjectiveThis study aimed to examine the applicability of the customised interactive computer education system (CICS) in patients who had a stroke in the early acute phase in order to determine the efficacy of the education system in (1) information satisfaction and (2) physiological related management compliance.MethodsThe prospective non-blinded randomised controlled study was conducted in an acute stroke unit of a local hospital in Hong Kong from March to August 2019. Chinese participants were selected if they were at least 18 years of age, experienced a minor stroke within 3 days. The exclusion criteria were communication problem and comorbidity with another acute disease. On the first day of admission, participants were allocated to the CICS and booklet groups, with each group comprising 50 participants. On the third day, the primary outcome, Modified Information Satisfaction Questionnaire for Acute Stroke (MISQ-S), was assessed.ResultsThere was a significant difference in ‘the need to improve information measures’ of the MISQ-S (p=0.04) between the CICS and booklet groups. The management compliance of these two groups did not have difference, but the CICS group had better clinical outcome, though not significant (p=0.387).ConclusionPatient education was needed and feasible in the early acute phase, and the CICS was more efficacious than the booklet. The positive results provided insights into and give a direction to the use of information technology in patient education.

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