Research on Recognition Method of Driving Fatigue State Based on Sample Entropy and Kernel Principal Component Analysis

In view of the nonlinear characteristics of electroencephalography (EEG) signals collected in the driving fatigue state recognition research and the issue that the recognition accuracy of the driving fatigue state recognition method based on EEG is still unsatisfactory, this paper proposes a driving fatigue recognition method based on sample entropy (SE) and kernel principal component analysis (KPCA), which combines the advantage of the high recognition accuracy of sample entropy and the advantages of KPCA in dimensionality reduction for nonlinear principal components and the strong non-linear processing capability. By using support vector machine (SVM) classifier, the proposed method (called SE_KPCA) is tested on the EEG data, and compared with those based on fuzzy entropy (FE), combination entropy (CE), three kinds of entropies including SE, FE and CE that merged with KPCA. Experiment results show that the method is effective

Research on the energy balance algorithm of WSN based on topology control

Abstract Topology control is a key energy-saving technology in the wireless-sensor networking. In this paper, networking-associated algorithm named local minimum spanning tree (LMST) employed while compromising on energy balance issues, leading to the short lifetime of the network, and proposes an energy balance algorithm. The algorithm fully investigates the energy utilization to link the data communication and the remaining energy of the communication node, to achieve the balanced energy, and to avoid the complexities associated with excessive consumption of energy when the intermediate node acts as a data forwarding task, thus extending the network life cycle. The simulated experimental outcomes showed a great potential of the proposed algorithm that can efficiently prolong the network lifetime and guarantee the long-term reliable operation of the network while ensuring other network performance indicators, such as throughput and delivery rate

Altered visual cortical processing in a mouse model of MECP2 duplication syndrome

As an epigenetic modulator of gene expression, Methyl-CpG binding protein 2 (MeCP2) is essential for normal neurological function. Dysfunction of MeCP2 is associated with a variety of neurological disorders. MECP2 gene duplication in human causes neuropsychiatric symptoms such as mental retardation and autism. MeCP2 overexpression in mice results in neurobehavioural disorders, dendritic abnormalities, and synaptic defects. However, how gain of MeCP2 function influences cortical processing of sensory information remains unclear. In this study, we examined visual processing in a mouse model of MECP2 duplication syndrome (MECP2 Tg1 mouse) at 8 and 14 weeks, which were before and after the onset of behavioural symptoms, respectively. In vivo extracellular recordings from primary visual cortex (V1) showed that neurons in Tg1 mice at both adult ages preferred higher spatial frequencies (SFs) than those in wild-type (WT) littermate controls, and the semi-saturation contrasts of neurons were lower in Tg1 mice at 8 weeks but not at 14 weeks. Behavioural experiments showed that the performance for visual detection at high SFs and low contrasts was higher in MECP2 Tg1 mice. Thus, MeCP2 gain-of-function in mice leads to higher visual acuity and contrast sensitivity, both at the levels of cortical response and behavioural performance

Ultrasound image-based deep learning to assist in diagnosing gross extrathyroidal extension thyroid cancer: a retrospective multicenter studyResearch in context

Summary: Background: The presence of gross extrathyroidal extension (ETE) in thyroid cancer will affect the prognosis of patients, but imaging examination cannot provide a reliable diagnosis for it. This study was conducted to develop a deep learning (DL) model for localization and evaluation of thyroid cancer nodules in ultrasound images before surgery for the presence of gross ETE. Methods: From January 2016 to December 2021 grayscale ultrasound images of 806 thyroid cancer nodules (4451 images) from 4 medical centers were retrospectively analyzed, including 517 no gross ETE nodules and 289 gross ETE nodules. 283 no gross ETE nodules and 158 gross ETE nodules were randomly selected from the internal dataset to form a training set and validation set (2914 images), and a multitask DL model was constructed for diagnosing gross ETE. In addition, the clinical model and the clinical and DL combined model were constructed. In the internal test set [974 images (139 no gross ETE nodules and 83 gross ETE nodules)] and the external test set [563 images (95 no gross ETE nodules and 48 gross ETE nodules)], the diagnostic performance of DL model was verified based on the pathological results. And then, compared the results with the diagnosis by 2 senior and 2 junior radiologists. Findings: In the internal test set, DL model demonstrated the highest AUC (0.91; 95% CI: 0.87, 0.96), which was significantly higher than that of two senior radiologists [(AUC, 0.78; 95% CI: 0.71, 0.85; P < 0.001) and (AUC, 0.76; 95% CI: 0.70, 0.83; P < 0.001)] and two juniors radiologists [(AUC, 0.65; 95% CI: 0.58, 0.73; P < 0.001) and (AUC, 0.69; 95% CI: 0.62, 0.77; P < 0.001)]. DL model was significantly higher than clinical model [(AUC, 0.84; 95% CI: 0.79, 0.89; P = 0.019)], but there was no significant difference between DL model and clinical and DL combined model [(AUC, 0.94; 95% CI: 0.91, 0.97; P = 0.143)]. In the external test set, DL model also demonstrated the highest AUC (0.88, 95% CI: 0.81, 0.94), which was significantly higher than that of one of senior radiologists [(AUC, 0.75; 95% CI: 0.66, 0.84; P = 0.008) and (AUC, 0.81; 95% CI: 0.72, 0.89; P = 0.152)] and two junior radiologists [(AUC, 0.72; 95% CI: 0.62, 0.81; P = 0.002) and (AUC, 0.67; 95 CI: 0.57, 0.77; P < 0.001]. There was no significant difference between DL model and clinical model [(AUC, 0.85; 95% CI: 0.79, 0.91; P = 0.516)] and clinical + DL model [(AUC, 0.92; 95% CI: 0.87, 0.96; P = 0.093)]. Using DL model, the diagnostic ability of two junior radiologists was significantly improved. Interpretation: The DL model based on ultrasound imaging is a simple and helpful tool for preoperative diagnosis of gross ETE thyroid cancer, and its diagnostic performance is equivalent to or even better than that of senior radiologists. Funding: Jiangxi Provincial Natural Science Foundation (20224BAB216079), the Key Research and Development Program of Jiangxi Province (20181BBG70031), and the Interdisciplinary Innovation Fund of Natural Science, Nanchang University (9167-28220007-YB2110)