Automatic classification of electrospun fiber polarized light images using mueller matrix depolarization parameter

Electrospun fibers are widely used in various fields of biology, medicine, and chemistry due to their unique morphological characteristics that determine their distinct application properties. Accurate and rapid classification of these fibers based on their morphology is critical for their effective utilization. Non-destructive and low-cost imaging methods are highly desirable for this purpose, so we obtained the polarization images of different forms of electrospun fibers (smooth surfaces, microporous, and beaded microspheres) by polarized light microscopy. In this study, we have explored the automatic classification of electrospun fibers based on their Mueller matrix depolarization parameter, which is highly correlated with the rough microporous structures on the surface of the object. To achieve this, we employed transfer learning and various convolutional neural networks (CNNs). Our proposed method outperformed the conventional approach that only utilizes a single Mueller matrix M44 image for classification, thus enabling researchers to effectively classify electrospun fibers. Given the high accuracy of our method, it may find significant utility in fields such as material science, nanotechnology, and bioengineering

Fully Implantable Neural Stimulator with Variable Parameters

Neural implantable systems have promoted the development of neurosurgery research and clinical practice. However, traditional tethered neural implants use physical wires for power supply and signal transmission, which have many restrictions on implant targets. Therefore, untethered, wireless, and controllable neural stimulation has always been widely recognized as the engineering goal of neural implants. In this paper, magnetically coupled resonant wireless power transfer (MCR-WPT) technology is adopted to design and manufacture a wireless stimulator for the electrical stimulation experiment of nerve repair. In the process of device development, SCM technology, signal modulation, demodulation, wireless power supply, and integration/packaging are used. Through experimental tests, the stimulator can output single-phase pulse signals with a variable frequency of (1–20 Hz), a duty cycle of (1–50%), and voltage. The average power is approximately 25 mW. The minimum pulse width of the signal is 200 μs and the effective distance of transmission is 1–4 cm. The stimulator can perform low-frequency, safe and controllable wireless stimulation

An Imbalanced Image Classification Method for the Cell Cycle Phase

The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we used the Wasserstein generative adversarial network-gradient penalty (WGAN-GP) for data augmentation. At the same time, a residual network (ResNet) was used for image classification. ResNet is one of the most used deep learning classification networks. The classification accuracy of cell cycle images was achieved more effectively with our method, reaching 83.88%. Compared with an accuracy of 79.40% in previous experiments, our accuracy increased by 4.48%. Another dataset was used to verify the effect of our model and, compared with the accuracy from previous results, our accuracy increased by 12.52%. The results showed that our new cell cycle image classification system based on WGAN-GP and ResNet is useful for the classification of imbalanced images. Moreover, our method could potentially solve the low classification accuracy in biomedical images caused by insufficient numbers of original images and the imbalanced distribution of original images

Automatic Detection of Age-Related Macular Degeneration Based on Deep Learning and Local Outlier Factor Algorithm

Age-related macular degeneration (AMD) is a retinal disorder affecting the elderly, and society’s aging population means that the disease is becoming increasingly prevalent. The vision in patients with early AMD is usually unaffected or nearly normal but central vision may be weakened or even lost if timely treatment is not performed. Therefore, early diagnosis is particularly important to prevent the further exacerbation of AMD. This paper proposed a novel automatic detection method of AMD from optical coherence tomography (OCT) images based on deep learning and a local outlier factor (LOF) algorithm. A ResNet-50 model with L2-constrained softmax loss was retrained to extract features from OCT images and the LOF algorithm was used as the classifier. The proposed method was trained on the UCSD dataset and tested on both the UCSD dataset and Duke dataset, with an accuracy of 99.87% and 97.56%, respectively. Even though the model was only trained on the UCSD dataset, it obtained good detection accuracy when tested on another dataset. Comparison with other methods also indicates the efficiency of the proposed method in detecting AMD

Superpixel-Oriented Label Distribution Learning for Skin Lesion Segmentation

Lesion segmentation is a critical task in skin cancer analysis and detection. When developing deep learning-based segmentation methods, we need a large number of human-annotated labels to serve as ground truth for model-supervised learning. Due to the complexity of dermatological images and the subjective differences of different dermatologists in decision-making, the labels in the segmentation target boundary region are prone to produce uncertain labels or error labels. These labels may lead to unsatisfactory performance of dermoscopy segmentation. In addition, the model trained by the errored one-hot label may be overconfident, which can lead to arbitrary prediction and model overfitting. In this paper, a superpixel-oriented label distribution learning method is proposed. The superpixels formed by the simple linear iterative cluster (SLIC) algorithm combine one-hot labels constraint and define a distance function to convert it into a soft probability distribution. Referring to the model structure of knowledge distillation, after Superpixel-oriented label distribution learning, we get soft labels with structural prior information. Then the soft labels are transferred as new knowledge to the lesion segmentation network for training. Ours method on ISIC 2018 datasets achieves an Dice coefficient reaching 84%, sensitivity 79.6%, precision 80.4%, improved by 19.3%, 8.6% and 2.5% respectively in comparison with the results of U-Net. We also evaluate our method on the tasks of skin lesion segmentation via several general neural network architectures. The experiments show that ours method improves the performance of network image segmentation and can be easily integrated into most existing deep learning architectures

A Novel Wearable Flexible Dry Electrode Based on Cowhide for ECG Measurement

The electrocardiogram (ECG) electrode, as a sensor, is an important part of the wearable ECG monitoring device. Natural leather is rarely used as the electrode substrate. In this paper, wearable flexible silver electrodes based on cowhide were prepared by sputtering and brush-painting. A signal generator, oscilloscope, impedance test instrument, and ECG monitor were used to build the test platform evaluating the performance of electrodes with six subjects. The lossless waveform transmission can be achieved with our electrodes. Therefore, the Pearson’s correlation coefficient calculated with input waveform and output waveform of the electrodes based on the top grain layer (GLE) and the split layer (SLE) of cowhide were 0.997 and 0.998 at 0.1 Hz respectively. The skin electrode impedance (Z) was tested, and the parameters of the equivalent circuit model of the skin electrode interface were calculated by a fitting method, indicating that the Z of the prepared electrodes was comparable with the standard gel electrode when the skin is moist enough. The signal-to-noise ratio of the ECG of the GLE and the SLE were 1.148 and 1.205 times that of the standard electrode in the standing posture, which meant the ECG measured by our electrodes was basically consistent with that measured by the standard electrode

A Strain Feedback Compensation Method during Cell Tensile Experiments

Cell tensile technique is an important and widely used tool in cell mechanical research. However, the strain control condition in traditional tensile experiments is not satisfied and would result in big errors. These strain errors will seriously impact the experimental accuracy and decrease the reliability and comparability of experimental results. In order to achieve the accurate strain control of the membrane during stretching, a strain feedback compensation method based on the digital image correlation is proposed in this paper. To evaluate the effect of the proposed compensation method, a series of stretching experiments in different strains ranging from 5% to 20% were performed. The results showed that our proposed method significantly decreased the errors of strain control. These results indicate that the strain feedback compensation method is very effective in controlling strain and can greatly improve the experimental accuracy

Magneto‐responsive polydimethylsiloxane films with magnetic powder: Characterization and potential application in the tensile loading system

Abstract Magnetic actuation has attracted a growing attention due to its non‐contact performance, rapid response and penetrability. The realization of magnetic actuation requires the participation of magneto‐responsive materials (MRMS), and magneto‐responsive polymers (MRPs) are one of the most widely used MRMs. Here, we report the fabrication, characterization and actuation tests of neodymium‐iron‐boron‐polydimethylsiloxane (NdFeB‐PDMS) magnetic films. The NdFeB‐PDMS magnetic film has great flexibility and elasticity, as well as excellent magnetism. The film also shows exceptional deformation and magnetic controllability under magnetic actuation. Besides, it can be driven under a very low magnetic field down to 1mT, and generate an out‐of‐plane displacement of 2.7 mm under the action of magnetic fields. We focus on the tensile deformation of NdFeB‐PDMS films under magnetic driving and provide a possible design for their potential application in the tensile loading system. Preliminary tests demonstrate that the size of tensile deformation is adjusted by controlling the output signal and amplification factor. Highlights Fabrication of NdFeB‐PDMS magnetic films. Mechanical and magnetic characterizations of NdFeB‐PDMS films. Magnetic actuation tests of NdFeB‐PDMS films. Magnetic field‐induced tensile deformation of NdFeB‐PDMS films. Potential application of NdFeB‐PDMS films in the tensile loading system

A Comprehensive Method for Accurate Strain Distribution Measurement of Cell Substrate Subjected to Large Deformation

Cell mechanical stretching in vitro is a fundamental technique commonly used in cardiovascular mechanobiology research. Accordingly, it is crucial to measure the accurate strain field of cell substrate under different strains. Digital image correlation (DIC) is a widely used measurement technique, which is able to obtain the accurate displacement and strain distribution. However, the traditional DIC algorithm used in digital image correlation engine (DICe) cannot obtain accurate result when utilized in large strain measurement. In this paper, an improved method aiming to acquire accurate strain distribution of substrate in large deformation was proposed, to evaluate the effect and accuracy, based on numerical experiments. The results showed that this method was effective and highly accurate. Then, we carried out uniaxial substrate stretching experiments and applied our method to measure strain distribution of the substrate. The proposed method could obtain accurate strain distribution of substrate film during large stretching, which would allow researchers to adequately describe the response of cells to different strains of substrate

LncRNA Expression Profile of Human Thoracic Aortic Dissection by High-Throughput Sequencing

Background/Aims: In this study, the long non-coding RNA (lncRNA) expression profile in human thoracic aortic dissection (TAD), a highly lethal cardiovascular disease, was investigated. Methods: Human TAD (n=3) and normal aortic tissues (NA) (n=3) were examined by high-throughput sequencing. Bioinformatics analyses were performed to predict the roles of aberrantly expressed lncRNAs. Quantitative real-time polymerase chain reaction (qRT-PCR) was applied to validate the results. Results: A total of 269 lncRNAs (159 up-regulated and 110 down-regulated) and 2, 255 mRNAs (1 294 up-regulated and 961 down-regulated) were aberrantly expressed in human TAD (fold-change> 1.5, P< 0.05). QRT-PCR results of five dysregulated genes were consistent with HTS data. A lncRNA-mRNA coexpression analysis showed positive correlations between the up-regulated lncRNA (ENSG00000269936) and its adjacent up-regulated mRNA (MAP2K6, R=0.940, P< 0.01), and between the down-regulated lncRNA_1421 and its down-regulated mRNAs (FBLN5, R=0.950, P< 0.01; ACTA2, R=0.96, P< 0.01; TIMP3, R=0.96, P< 0.05). The lncRNA-miRNA-mRNA network indicated that the up-regulated lncRNA XIST and p21 had similar sequences targeted by has-miR-17-5p. The results of luciferase assay and fluorescence immuno-cytochemistry were consistent with that. And qRT-PCR results showed that lncRNA XIST and p21 were expressed at a higher level and has-miR-17-5p was expressed at a lower level in TAD than in NA. The predicted binding motifs of three up-regulated lncRNAs (ENSG00000248508, ENSG00000226530, and EG00000259719) were correlated with up-regulated RUNX1 (R=0.982, P< 0.001; R=0.967, P< 0.01; R=0.960, P< 0.01, respectively). Conclusions: Our study revealed a set of dysregulated lncRNAs and predicted their multiple potential functions in human TAD. These findings suggest that lncRNAs are novel potential therapeutic targets for human TAD