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"Hong, Feng"
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Sleep apnea detection from a single-lead ECG signal with automatic feature-extraction through a modified LeNet-5 convolutional neural network
Sleep apnea (SA) is the most common respiratory sleep disorder, leading to some serious neurological and cardiovascular diseases if left untreated. The diagnosis of SA is traditionally made using Polysomnography (PSG). However, this method requires many electrodes and wires, as well as an expert to monitor the test. Several researchers have proposed instead using a single channel signal for SA diagnosis. Among these options, the ECG signal is one of the most physiologically relevant signals of SA occurrence, and one that can be easily recorded using a wearable device. However, existing ECG signal-based methods mainly use features (i.e. frequency domain, time domain, and other nonlinear features) acquired from ECG and its derived signals in order to construct the model. This requires researchers to have rich experience in ECG, which is not common. A convolutional neural network (CNN) is a kind of deep neural network that can automatically learn effective feature representation from training data and has been successfully applied in many fields. Meanwhile, most studies have not considered the impact of adjacent segments on SA detection. Therefore, in this study, we propose a modified LeNet-5 convolutional neural network with adjacent segments for SA detection. Our experimental results show that our proposed method is useful for SA detection, and achieves better or comparable results when compared with traditional machine learning methods.
Journal Article
Underwater Acoustic Target Recognition with a Residual Network and the Optimized Feature Extraction Method
Underwater Acoustic Target Recognition (UATR) remains one of the most challenging tasks in underwater signal processing due to the lack of labeled data acquisition, the impact of the time-space varying intrinsic characteristics, and the interference from other noise sources. Although some deep learning methods have been proven to achieve state-of-the-art accuracy, the accuracy of the recognition task can be improved by designing a Residual Network and optimizing feature extraction. To give a more comprehensive representation of the underwater acoustic signal, we first propose the three-dimensional fusion features along with the data augment strategy of SpecAugment. Afterward, an 18-layer Residual Network (ResNet18), which contains the center loss function with the embedding layer, is designed to train the aggregated features with an adaptable learning rate. The recognition experiments are conducted on the ship-radiated noise dataset from a real environment, and the accuracy results of 94.3% indicate that the proposed method is appropriate for underwater acoustic recognition problems and sufficiently surpasses other classification methods.
Journal Article
Organs-without-body: a study on the genealogy of vision leading to the posthuman age
In 1834, an American medical missionary, Peter Parker, arrived in Guangdong and established a hospital. He used his medical skills to evangelise and commissioned a local artist named Lam Qua to paint numerous medical portraits. Previous studies on these portraits have mainly focused on historiography or critical discourse studies, with little attention given to the visual thinking behind them. Therefore, this paper uses Foucault’s
archaeology of knowledge
to systematically sort out similar images from different periods to demonstrate how the epistemological type brought by medical observation operates and is represented through discursive formation. The study shows that Lam Qua’s medical portraits can be traced back to clinicians’ records of cases and served as ethnographic displays, similar to freak shows. Information gradually becomes emphasised partially due to its detachment from the entity in the dissemination process. This idea inspired Rei Kawakubo’s clothing fashions, while Cunningham’s dance art takes it further by abstracting information from the body, visually representing posthuman thinking. These seemingly unrelated cases belong to the same collection of statements that form a system called
organs-without-body
in this paper, serving as a metaphor for non-physical information to help us better understand the formation of posthuman thought.
Journal Article
Effects of Betaine on LPS-Stimulated Activation of Microglial M1/M2 Phenotypes by Suppressing TLR4/NF-κB Pathways in N9 Cells
Microglia mediate multiple facets of neuroinflammation. They can be phenotypically divided into a classical phenotype (pro-inflammatory, M1) or an alternative phenotype (anti-inflammatory, M2) with different physiological characteristics and biological functions in the inflammatory process. Betaine has been shown to exert anti-inflammatory effects. In this study, we aimed to verify the anti-inflammatory effects of betaine and elucidate its possible molecular mechanisms of action in vitro. Lipopolysaccharide (LPS)-activated microglial cells were used as an inflammatory model to study the anti-inflammatory efficacy of betaine and explore its mechanism of regulating microglial polarisation by investigating the morphological changes and associated inflammatory changes. Cytokine and inflammatory mediator expression was also measured by ELISA, flow cytometry, immunofluorescence, and western blot analysis. Toll-like receptor (TLR)-myeloid differentiation factor 88 (Myd88)-nuclear factor-kappa B (NF-κB) p65, p-NF-κB p65, IκB, p-IκB, IκB kinase (IKK), and p-IKK expression was determined by western blot analysis. Betaine significantly mitigated the production of pro-inflammatory cytokines and increased the release of anti-inflammatory cytokines. It promoted the conversion of the microglia from M1 to M2 phenotype by decreasing the expression of inducible nitric oxide synthase and CD16/32 and by increasing that of CD206 and arginase-1. Betaine treatment inhibited the TLR4/NF-κB pathways by attenuating the expression of TLR4-Myd88 and blocking the phosphorylation of IκB and IKK. In conclusion, betaine could significantly alleviate LPS-induced inflammation by regulating the polarisation of microglial phenotype; thus, it might be an effective therapeutic agent for neurological disorders.
Journal Article
Boosting the catalysis of gold by O2 activation at Au-SiO2 interface
Supported gold (Au) nanocatalysts have attracted extensive interests in the past decades because of their unique catalytic properties for a number of key chemical reactions, especially in (selective) oxidations. The activation of O
2
on Au nanocatalysts is crucial and remains a challenge because only small Au nanoparticles (NPs) can effectively activate O
2
. This severely limits their practical application because Au NPs inevitably sinter into larger ones during reaction due to their low Taman temperature. Here we construct a Au-SiO
2
interface by depositing thin SiO
2
layer onto Au/TiO
2
and calcination at high temperatures and demonstrate that the interface can be not only highly sintering resistant but also extremely active for O
2
activation. This work provides insights into the catalysis of Au nanocatalysts and paves a way for the design and development of highly active supported Au catalysts with excellent thermal stability.
The development of sintering resistant supported Au catalysts with high activity still remains a challenge. Here the authors construct a Au-SiO
2
interface by depositing SiO
2
thin layer onto Au/TiO
2
catalyst which shows very high activity in CO oxidation even after calcination at 800 °C.
Journal Article
Human Activity Sensing with Wireless Signals: A Survey
Wireless networks have been widely deployed with a high demand for wireless data traffic. The ubiquitous availability of wireless signals brings new opportunities for non-intrusive human activity sensing. To enhance a thorough understanding of existing wireless sensing techniques and provide insights for future directions, this survey conducts a review of the existing research on human activity sensing with wireless signals. We review and compare existing research of wireless human activity sensing from seven perspectives, including the types of wireless signals, theoretical models, signal preprocessing techniques, activity segmentation, feature extraction, classification, and application. With the development and deployment of new wireless technology, there will be more sensing opportunities in human activities. Based on the analysis of existing research, the survey points out seven challenges on wireless human activity sensing research: robustness, non-coexistence of sensing and communications, privacy, multiple user activity sensing, limited sensing range, complex deep learning, and lack of standard datasets. Finally, this survey presents four possible future research trends, including new theoretical models, the coexistence of sensing and communications, awareness of sensing on receivers, and constructing open datasets to enable new wireless sensing opportunities on human activities.
Journal Article
Improved therapeutic potential of MSCs by genetic modification
Mesenchymal stem cells (MSCs), well-studied adult stem cells in various tissues, possess multi-lineage differentiation potential and anti-inflammatory properties. MSCs have been approved to regenerate lineage-specific cells to replace injured cells in tissues. MSCs are approved to treat inflammatory diseases. With the discovery of genes important for the repair of damaged tissues, MSCs genetically modified by such genes hold improved therapeutic potential. In this review, we summarised the uses of genetically modified MSCs to treat different diseases, including bone diseases, cardiovascular diseases, autoimmune diseases, central nervous system disorders, and cancer. To better understand the exact role of genetically modified MSCs, key mechanisms determining, which genes are selected to be used for modifying MSCs and improvements in post-genetic modification are discussed. Therapeutic benefits enhanced by genetic modifications are to be documented by further clinical studies.
Journal Article