BiVi-GAN: Bivariate Vibration GAN.

In the domain of prognosis and health management (PHM) for rotating machinery, the criticality of ensuring equipment reliability cannot be overstated. With developments in artificial intelligence (AI) and deep learning, there have been numerous attempts to use those methodologies in PHM. However, there are challenges to applying them in practice because they require huge amounts of data. This study explores a novel approach to augment vibration data-a primary component in traditional PHM methodologies-using a specialized generative model. Recognizing the limitations of deep learning models, which often fail to capture the intrinsic physical characteristics vital for vibration analysis, we introduce the bivariate vibration generative adversarial networks (BiVi-GAN) model. BiVi-GAN incorporates elements of a physics-informed neural network (PINN), emphasizing the specific vibration characteristics of rotating machinery. We integrate two types of physical information into our model: order analysis and cross-wavelet transform, which are crucial for dissecting the vibration characteristics of such machinery. Experimental findings show the effectiveness of our proposed model. With the incorporation of physics information (PI) input and PI loss, the BiVi-GAN showed a 70% performance improvement in terms of JS divergence compared with the baseline biwavelet-GAN model. This study maintains the potential and efficacy of complementary domain-specific insights with data-driven AI models for more robust and accurate outcomes in PHM.

Time Series Classification with InceptionFCN.

Deep neural networks (DNN) have proven to be efficient in computer vision and data classification with an increasing number of successful applications. Time series classification (TSC) has been one of the challenging problems in data mining in the last decade, and significant research has been proposed with various solutions, including algorithm-based approaches as well as machine and deep learning approaches. This paper focuses on combining the two well-known deep learning techniques, namely the Inception module and the Fully Convolutional Network. The proposed method proved to be more efficient than the previous state-of-the-art InceptionTime method. We tested our model on the univariate TSC benchmark (the UCR/UEA archive), which includes 85 time-series datasets, and proved that our network outperforms the InceptionTime in terms of the training time and overall accuracy on the UCR archive.

Extreme Low-Light Image Enhancement for Surveillance Cameras Using Attention U-Net.

Low-light image enhancement is one of the most challenging tasks in computer vision, and it is actively researched and used to solve various problems. Most of the time, image processing achieves significant performance under normal lighting conditions. However, under low-light conditions, an image turns out to be noisy and dark, which makes subsequent computer vision tasks difficult. To make buried details more visible, and reduce blur and noise in a low-light captured image, a low-light image enhancement task is necessary. A lot of research has been applied to many different techniques. However, most of these approaches require much effort or expensive equipment to perform low-light image enhancement. For example, the image has to be captured in a raw camera file in order to be processed, and the addressing method does not perform well under extreme low-light conditions. In this paper, we propose a new convolutional network, Attention U-net (the integration of an attention gate and a U-net network), which is able to work on common file types (.PNG, .JPEG, .JPG, etc.) with primary support from deep learning to solve the problem of surveillance camera security in smart city inducements without requiring the raw image file from the camera, and it can perform under the most extreme low-light conditions.

An absorbable thread suture technique to treat snoring.

We investigated a novel, minimally invasive surgery that uses an absorbable suture technique to treat snoring and mild obstructive sleep apnea. This simple procedure was developed to increase the stiffness of the soft palate and to promote uvula elevation with sutures. Thirty-five snorer and mild obstructive sleep apnea syndrome patients were included in this study. The palate was sutured with the newly developed technique. The results of our surgery were evaluated using polysomnography (PSG), the Epworth sleepiness scale (ESS), and a visual analogue scale (VAS) before surgery and 90 days after surgery. One year after surgery, telephone interviews were performed to assess patient satisfaction. Postoperative physical examinations of all patients showed increased stiffness of the soft palate and superiorly displaced uvula. These findings were consistent after the postoperative day 90. The patients' snoring symptoms and their bed partners' complaints, assessed by ESS and VAS, significantly improved compared to the pre-treatment value (p < 0.05). Additionally, the apnea-hypopnea index (AHI), assessed by PSG, was significantly improved compared to the pre-treatment value (p < 0.05). Based on the results from the telephone interviews analyzed 1 year after surgery, about 88 % of patients were satisfied with the outcome. This minimally invasive snoreplasty that uses absorbable suture material is an effective and simple procedure for treating snoring and mild obstructive sleep apnea.

Engraftment and regenerative effects of bone marrow stromal cell transplantation on damaged rat olfactory mucosa.

To develop a new therapeutic method to treat olfactory deficits, we investigated the engraftment and regenerative effects of transplanted bone marrow stromal cells (BMSCs) on damaged rat olfactory mucosa. To induce olfactory nerve degeneration, one side of the olfactory mucosa of Sprague-Dawley rats was damaged via Triton X-100 irrigation. Phosphate-buffered saline containing syngeneic BMSCs was injected into the olfactory mucosa for transplantation. PKH fluorescent cell dye labeling of BMSCs was used to monitor the transplanted cells. After transplantation of BMSCs, the thickness and regeneration of olfactory mucosa were analyzed using hematoxylin-eosin (H&E) staining. S100 immunohistochemical staining was used to measure nerve sheath regeneration. The increase in NGF (nerve growth factor) level in the olfactory mucosa was measured by Western blot analysis. Transplanted bone marrow stromal cells were engrafted to the lamia propria of damaged mucosa. The mean time for normalization of thickness and morphological recovery of the olfactory mucosa was 4 weeks in the therapeutic group and 9 weeks in the control group. S100 immunoreactivity was higher on the BMSC-treated side than on the control side. During regeneration, the expression of NGF increased in the olfactory mucosa of the experimental group. Based on these results, BMSC transplantation accelerated regeneration of olfactory mucosa damaged by Triton X-100, and NGF may be essential to this regenerative process.

Impulse Classification Network for Video Head Impulse Test.

The vestibulo-ocular reflex (VOR) is a dynamic system of the human brain that helps to maintain balance and to stabilize vision during head movement. The video head impulse test (vHIT) is a clinical test that uses lightweight, high-speed video goggles to examine the VOR function by calculating the ratio of eye-movement to head-movement velocities. The main problem with a patient's vHIT is that data coming from the goggles may have artifacts and other noise. This paper proposes an impulse classification network (ICN) using a one-dimensional convolutional neural network that can detect noisy data and classify human VOR impulses. Our ICN found actual classes of a patient's impulses with 95% accuracy.Clinical Relevance-ICN is a high-performance classification method that works on a patient's vHIT impulse data by identifying abnormalities and artifacts. This method is an advanced clinical decision support system that can help doctors quickly make decisions.

Correlation of soft palate length with velum obstruction and severity of obstructive sleep apnea syndrome.

OBJECTIVE: Our aim in this study was to analyze whether soft palate length and velum obstruction during sleep are correlated and to determine the effects of related parameters on obstructive sleep apnea syndrome (OSAS) severity. We used computed tomography to measure soft palate length and drug-induced sleep endoscopy (DISE) to evaluate velum obstruction severity. Patients also underwent polysomnography (PSG) for evaluation of OSAS severity. METHODS: A retrospective cohort of 67 patients with OSAS treated between May 1st, 2013 and July 31st, 2016 was analyzed. Each patient underwent DISE, PSG, and computed tomography. Using DISE, velum obstruction was categorized by the VOTE classification method. Using computed tomography, soft palate length was measured as the length of the posterior nasal spine to the uvula. Correlations of velum obstruction in DISE and PSG parameters (obstructive apnea, hypopnea, apnea hypopnea index (AHI), respiratory effort related arousal (RERA), respiratory disturbance index (RDI), baseline SaO2, and minimum SaO2) with soft palate length were also analyzed. RESULTS: Among the 67 patients, the average PNS-U length was 39.90±4.19mm. Length was significantly different by age but not by other demographic characteristics such as sex, past history, or BMI. DISE revealed a statistically significant difference of velum obstruction degree; the cutoff value for PNS-U was 39.47mm. The PSG results, obstructive apnea, AHI, RDI, baseline SaO2, and minimum SaO2 were correlated with PNS-U length, while other results such as hypopnea and RERA showed no correlation. CONCLUSION: Analysis of soft palate length showed that increased PNS-U length was associated with higher rates of obstructive apnea, AHI, and RDI as assessed by PSG. In contrast, lower baseline SaO2 and minimum SaO2 values were seen by PSG; more severe velum obstruction was seen by DISE. We propose that when a soft palate is suspected in OSAS, computed tomography measurement of soft palate length is a valid method for estimating the degree of velum obstruction and the severity of OSAS.

Tissue-engineered bone formation with gene transfer and mesenchymal stem cells in a minimally invasive technique.

BACKGROUND: The objective of this study was to use a chitosan-alginate gel to implant bone marrow-derived mesenchymal stem cells subcutaneously in a minimally invasive manner and promote bone formation by the simultaneously transferred osteogenic protein (OP)-1 (bone morphogenic protein-7) gene. METHOD AND RESULTS: The complex of polyethylenimine/luciferase plasmid DNA embedded in the gel was able to transfect HEK 293 cells on a culture dish or co-encapsulated in the gel. When injected into the subcutaneous space of mice, luciferase expression was two to three orders of magnitude increased above the background. To examine the efficacy of gene-, cell-, and combined gene- and cell-encapsulated gels in tissue generation, samples were injected into the subcutaneous space of 6-week-old athymic nude mice, and the OP-1 plasmid was studied. At 8 weeks after the injection, the gels only maintained their volumetric shape when human mesenchymal stem cells (hMSCs) were encapsulated, but otherwise the gels were partially dissolved. Transgene expression of OP-1 was clearly detected in the samples after 4 weeks but not after 8 weeks. Type II collagen was detected in all the gels containing the OP-1 plasmid, with or without hMSCs. The samples with the combination of OP-1 DNA and hMSCs revealed strong type II collagen expression as well as osteoid foci. CONCLUSION: These results suggest that combined gene and hMSC delivery within a chitosan-alginate gel could be an interesting approach for tissue engineering.

A new nose tip-defining technique for Asians using autologous cartilage prefabricated with octyl-2-cyanoacrylate adhesive.

OBJECTIVES: To form and test a prefabricated, 3-dimensional tip graft structure from autologous cartilage pieces and octyl-2-cyanoacrylate adhesive (Dermabond). Displacement and instability of grafted cartilage are common drawbacks of endonasal tip surgery, and we evaluated the feasibility and efficacy of octyl-2-cyanoacrylate adhesive for clinical use. METHODS: Three New Zealand rabbits were examined in this study. We cut auricular cartilage into pieces of approximately 1 x 2 cm and glued them together with octyl-2-cyanoacrylate. Three months after subcutaneous implantation in the rabbits, we histopathologically analyzed foreign body reactions, histotoxic effects, and the structure of the regenerative tissue in the implanted cartilage. Furthermore, from human patients we harvested small pieces of autologous cartilage and cemented them together with octyl-2-cyanoacrylate adhesive to prefabricate 3-dimensional tip grafts, which we then inserted into the nasal tips of patients via an endonasal approach to project and contour the nasal tip. The changes in tip definition of 42 Asian patients were analyzed. RESULTS: Octyl-2-cyanoacrylate adhesive interacts well with cartilage tissues with no systemic or local adverse effects. Its ability to bond cartilage instantaneously allows for the fabrication of intricate cartilage implant components. The aesthetic results in 42 cases were excellent, and the implants were both safe and effective. CONCLUSION: Our results demonstrate that octyl-2-cyanoacrylate can be safely used in endonasal tip surgery to produce excellent cosmetic outcomes in Asian subjects.