| 1. |
- Beal, Jacob, et al.
(author)
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Robust estimation of bacterial cell count from optical density
- 2020
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In: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 3:1
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Journal article (peer-reviewed)abstract
- Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data.
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| 2. |
- Zhang, H., et al.
(author)
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Low-Cost and Confidential ECG Acquisition Framework Using Compressed Sensing and Chaotic Systems for Wireless Body Area Network
- 2022
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In: IEEE journal of biomedical and health informatics. - : IEEE. - 2168-2194 .- 2168-2208. ; 26:12, s. 5783-5792
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Journal article (peer-reviewed)abstract
- Recent years have witnessed an increasing popularity of wireless body area network (WBAN), with which continuous collection of physiological signals can be conveniently performed for healthcare monitoring. Energy consumption is a critical issue because it directly affects the duration of the equipped sensors. In this paper, we propose a low-cost and confidential electrocardiogram (ECG) acquisition approach for WBAN. The compressed sensing (CS) is employed for low-cost signal acquisition, and its cryptographic features are exploited for promoting the framework's confidentiality. In particular, the RIPless measurement matrix is used to give CS the resistance against plaintext attack, while the first-order $\Sigma \Delta$ quantizer is employed to embed the cryptographic diffusion feature into the whole system. Two chaotic systems are employed for generating the required secret elements for the acquisition and encryption. Experiment results well demonstrate the signal reconstruction and security performance of the proposed framework.
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| 3. |
- Gao, Kun, 1993, et al.
(author)
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Modeling Measurements Towards Effect of Past Behavior on Travel Behavior
- 2021
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In: Smart Innovation, Systems and Technologies. - Singapore : Springer Singapore. - 2190-3026 .- 2190-3018. ; 231, s. 141-157
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Conference paper (peer-reviewed)abstract
- The inertia effect of past behavior has attracted attention in the travel behavioral literature because of its bearing on travel choice modeling. Several measurements have been proposed to model the inertia effects. However, no consensus concerning appropriate modelling methods is reached, which leads to potential biases in analysis. The study aims to conduct a comprehensive investigation of modeling measurements regarding inertia effects of past behavior from the perspectives of estimation, behavioral indications and predictions. Differing from existing literature that only focused on estimation performance, we examine the performances of different methods in predictions and behavioral interpretations. To our best knowledge, these aspects are not investigated in the literature based on empirical data. The necessary information for constructing the measurements, underlying consumption, significance in estimation, behaviorally implausible issue, performances in estimation and predictions for these measurements are all compared based on behavioral data. The results shed lights on performances and suitability of different measurements for inertia effects in terms of estimation, behavioral interpretation and prediction, which support the further investigations of past behavior on travelers’ choice behavior.
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| 4. |
- Niu, Qinwang, et al.
(author)
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Toward the Internet of Medical Things : Architecture, trends and challenges
- 2024
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In: Mathematical Biosciences and Engineering. - : American Institute of Mathematical Sciences. - 1547-1063 .- 1551-0018. ; 21:1, s. 650-678
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Journal article (peer-reviewed)abstract
- In recent years, the growing pervasiveness of wearable technology has created new opportunities for medical and emergency rescue operations to protect users' health and safety, such as cost-effective medical solutions, more convenient healthcare and quick hospital treatments, which make it easier for the Internet of Medical Things (IoMT) to evolve. The study first presents an overview of the IoMT before introducing the IoMT architecture. Later, it portrays an overview of the core technologies of the IoMT, including cloud computing, big data and artificial intelligence, and it elucidates their utilization within the healthcare system. Further, several emerging challenges, such as cost-effectiveness, security, privacy, accuracy and power consumption, are discussed, and potential solutions for these challenges are also suggested.
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| 5. |
- Teng, Fei, et al.
(author)
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Multimedia Monitoring System of Obstructive Sleep Apnea via Deep Active Learning Model
- 2022
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In: IEEE Multimedia. - : IEEE. - 1070-986X .- 1941-0166. ; 29:3, s. 48-56
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Journal article (peer-reviewed)abstract
- Obstructive Sleep Apnea (OSA) is one of the most common sleep-related breathing disorders. Nearly 1 billion people worldwide suffer from it, causing serious health effects and social burden. However, traditional monitoring systems often fall short in terms of cost and accessibility. In this article, we first propose a deep active learning model to detect OSA events from electrocardiogram (ECG). We then designed and developed a prototype of OSA monitoring system using ECG sensor and smartphone, in which our OSA detection algorithm is implemented and validated. Experiments show that we achieve accuracy of 92.15% while using 40% of labeled data, significantly reducing the cost of labeling and maximizing the performance. According to detection results and health-related multimedia signals, we provide OSA risk level and medical advice to users. We believe that the multimedia monitoring system can efficiently help diagnose OSA, which could lead to effective intervention strategies and better sleep care.
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| 6. |
- Wang, Shudong, et al.
(author)
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MSHGANMDA : Meta-Subgraphs Heterogeneous Graph Attention Network for miRNA-Disease Association Prediction
- 2023
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In: IEEE journal of biomedical and health informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 2168-2194 .- 2168-2208. ; 27:10, s. 4639-4648
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Journal article (peer-reviewed)abstract
- MicroRNAs (miRNAs) influence several biological processes involved in human disease. Biological experiments for verifying the association between miRNA and disease are always costly in terms of both money and time. Although numerous biological experiments have identified multi-types of associations between miRNAs and diseases, existing computational methods are unable to sufficiently mine the knowledge in these associations to predict unknown associations. In this study, we innovatively propose a heterogeneous graph attention network model based on meta-subgraphs (MSHGATMDA) to predict the potential miRNA-disease associations. Firstly, we define five types of meta-subgraph from the known miRNA-disease associations. Then, we use meta-subgraph attention and meta-subgraph semantic attention to extract features of miRNA-disease pairs within and between these five meta-subgraphs, respectively. Finally, we apply a fully-connected layer (FCL) to predict the scores of unknown miRNA-disease associations and cross-entropy loss to train our model end-to-end. To evaluate the effectiveness of MSHGATMDA, we apply five-fold cross-validation to calculate the mean values of evaluation metrics Accuracy, Precision, Recall, and F1-score as 0.8595, 0.8601, 0.8596, and 0.8595, respectively. Experiments show that our model, which primarily utilizes multi-types of miRNAdisease association data, gets the greatest ROC-AUC value of 0.934 when compared to other state-of-the-art approaches. Furthermore, through case studies, we further confirm the effectiveness of MSHGATMDA in predicting unknown diseases.
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| 7. |
- Wei, Bo’an, et al.
(author)
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Construction site hazard identification and worker adverse reaction monitoring using electroencephalograms : a review
- 2024
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In: Buildings. - : MDPI. - 2075-5309. ; 14:1
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Research review (peer-reviewed)abstract
- The construction process is a dynamic one, and the complexity of the working conditions and the high level of uncertainty make the construction industry the third most dangerous industry after mining and agriculture. And since the construction industry is vital to the development of a country, safety during construction is of particular importance. A great deal of research, studies and practices have been conducted to reduce potential risks and improve worker efficiency during the construction process. In recent years, with the rapid development of cognitive neuroscience and the integration of medical technology, various wearable monitoring devices have been widely used in the field of building construction for real-time monitoring of workers’ physical and mental conditions. Among them, the application of EEG (electroencephalogram) in the building construction process enables researchers to gain insight into the physical and mental state of construction workers while performing construction tasks. This paper introduces EEG technology and portable EEG monitoring equipment and summarizes its application in monitoring workers’ adverse reactions (emotion, fatigue, psychological burden, and vigilance) and construction hazard identification during the process of construction in recent years, which provides future EEG research in the field of building construction and construction site safety management.
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| 8. |
- Yu, X., et al.
(author)
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Cardiac LGE MRI Segmentation with Cross-Modality Image Augmentation and Improved U-Net
- 2023
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In: IEEE journal of biomedical and health informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 2168-2194 .- 2168-2208. ; 27:2, s. 588-597
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Journal article (peer-reviewed)abstract
- Image segmentation is a challenging problem in imaging informatics, which stems from the intersection of imaging techniques, computer science and biomedicine. In particular, accurate segmentation of cardiac structures in late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) is of great clinical importance for cardiac function assessment and myocardial disease diagnosis. However, it is a well-known challenge due to its special imaging modality and the lack of labeled LGE samples. In this paper, we propose an unsupervised ventricular segmentation algorithm that can perform biventricular segmentation of LGE images in the absence of labeled LGE data. There are two primary modules, the data augmentation procedure and the segmentation network. The easily available annotated balanced-Steady State Free Precession (bSSFP) images are employed for cross-modal data augmentation by image translation, where a single bSSFP image is converted into multiple synthetic LGE images while preserving the original morphological structure. Then, the proposed segmentation network is trained with the synthetic LGE images and used for segmenting real LGE images. Validation experiments demonstrated the effectiveness and advantages of the proposed algorithm.
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| 9. |
- Zhang, Haiwen, et al.
(author)
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Biologically inspired flexible photonic films for efficient passive radiative cooling
- 2020
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In: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 0027-8424 .- 1091-6490. ; 117:26, s. 14657-14666
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Journal article (peer-reviewed)abstract
- Temperature is a fundamental parameter for all forms of lives. Natural evolution has resulted in organisms which have excellent thermoregulation capabilities in extreme climates. Bioinspired materials that mimic biological solution for thermoregulation have proven promising for passive radiative cooling. However, scalable production of artificial photonic radiators with complex structures, outstanding properties, high throughput, and low cost is still challenging. Herein, we design and demonstrate biologically inspired photonic materials for passive radiative cooling, after discovery of longicorn beetles' excellent thermoregulatory function with their dual-scale fluffs. The natural fluffs exhibit a finely structured triangular cross-section with two thermoregulatory effects which effectively reflects sunlight and emits thermal radiation, thereby decreasing the beetles' body temperature. Inspired by the finding, a photonic film consisting of a micropyramid-arrayed polymer matrix with random ceramic particles is fabricated with high throughput. The film reflects similar to 95% of solar irradiance and exhibits an infrared emissivity >0.96. The effective cooling power is found to be similar to 90.8 W center dot m(-2) and a temperature decrease of up to 5.1 degrees C is recorded under direct sunlight. Additionally, the film exhibits hydrophobicity, superior flexibility, and strong mechanical strength, which is promising for thermal management in various electronic devices and wearable products. Our work paves the way for designing and fabrication of high-performance thermal regulation materials.
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| 10. |
- Zhang, Li-bo, et al.
(author)
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Self-adaptive reconstruction for compressed sensing based ECG acquisition in wireless body area network
- 2023
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In: Future Generation Computer Systems. - : Elsevier. - 0167-739X .- 1872-7115. ; 142, s. 228-236
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Journal article (peer-reviewed)abstract
- The compressed sensing (CS) has been demonstrated as a promising solution for low-cost signal acquisition in wireless body area network. In this paper, a novel signal reconstruction scheme based on adaptive dictionary and matched filtering in CS domain is proposed for the ECG acquisition. The proposed method selects adaptive overcomplete dictionary based on the QRS estimation of the compressed measurements in each frame. If a QRS complex is estimated in this frame, an adaptive overcomplete dictionary matching the QRS characteristics of this frame is selected for reconstruction, otherwise, a dictionary trained by segments without QRS complex is selected. The ECG frames whose estimated QRS complexes locate in several consecutive locations, the so-called region width, are considered as one category, and will be reconstructed by one overcomplete dictionary which is trained by similar ECG waves. Extensive experiments have been conducted, and the results well demonstrate the effectiveness for signal reconstruction as well as its advantages over some state-of-the-art algorithms.(c) 2022 Elsevier B.V. All rights reserved.
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