Granulocyte colony-stimulating factor treatment ameliorates lupus nephritis through the expansion of regulatory T cells

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Abstract Background Granulocyte colony-stimulating factor (G-CSF) can induce regulatory T cells (Tregs) as well as myeloid-derived suppressor cells (MDSCs). Despite the immune modulatory effects of G-CSF, results of G-CSF treatment in systemic lupus erythematosus are still controversial. We therefore investigated whether G-CSF can ameliorate lupus nephritis and studied the underlying mechanisms. Methods NZB/W F1 female mice were treated with G-CSF or phosphate-buffered saline for 5 consecutive days every week from 24 weeks of age, and were analyzed at 36 weeks of age. Results G-CSF treatment decreased proteinuria and serum anti-dsDNA, increased serum complement component 3 (C3), and attenuated renal tissue injury including deposition of IgG and C3. G-CSF treatment also decreased serum levels of BUN and creatinine, and ultimately decreased mortality of NZB/W F1 mice. G-CSF treatment induced expansion of CD4+CD25+Foxp3+ Tregs, with decreased renal infiltration of T cells, B cells, inflammatory granulocytes and monocytes in both kidneys and spleen. G-CSF treatment also decreased expression levels of MCP-1, IL-6, IL-2, and IL-10 in renal tissues as well as serum levels of MCP-1, IL-6, TNF-α, IL-10, and IL-17. When Tregs were depleted by PC61 treatment, G-CSF-mediated protective effects on lupus nephritis were abrogated. Conclusions G-CSF treatment ameliorated lupus nephritis through the preferential expansion of CD4+CD25+Foxp3+ Tregs. Therefore, G-CSF has a therapeutic potential for lupus nephritis

WSN-Based Height Estimation of Moving Object in Surveillance Systems

In the WSN- (wireless sensor network-) based surveillance system to detect undesired intrusion, all detected objects are not intruders. In order to reduce false alarms, human detection mechanism needs to determine if the detected object is a human. For human detection, physical characteristics of human are usually used. In this paper, we use the physical height to differentiate an intruder from detected objects. Using the measured information from sensors, we estimate the height of the detected object. Based on the height, if the detected object is decided as an intruder, an alarm is given to a control center. The experimental results indicate that our mechanism correctly and fast estimates the height of the object without complex computation

A Methodology of Condition Monitoring System Utilizing Supervised and Semi-Supervised Learning in Railway

In this paper, research was conducted on anomaly detection of wheel flats. In the railway sector, conducting tests with actual railway vehicles is challenging due to safety concerns for passengers and maintenance issues as it is a public industry. Therefore, dynamics software was utilized. Next, STFT (short-time Fourier transform) was performed to create spectrogram images. In the case of railway vehicles, control, monitoring, and communication are performed through TCMS, but complex analysis and data processing are difficult because there are no devices such as GPUs. Furthermore, there are memory limitations. Therefore, in this paper, the relatively lightweight models LeNet-5, ResNet-20, and MobileNet-V3 were selected for deep learning experiments. At this time, the LeNet-5 and MobileNet-V3 models were modified from the basic architecture. Since railway vehicles are given preventive maintenance, it is difficult to obtain fault data. Therefore, semi-supervised learning was also performed. At this time, the Deep One Class Classification paper was referenced. The evaluation results indicated that the modified LeNet-5 and MobileNet-V3 models achieved approximately 97% and 96% accuracy, respectively. At this point, the LeNet-5 model showed a training time of 12 min faster than the MobileNet-V3 model. In addition, the semi-supervised learning results showed a significant outcome of approximately 94% accuracy when considering the railway maintenance environment. In conclusion, considering the railway vehicle maintenance environment and device specifications, it was inferred that the relatively simple and lightweight LeNet-5 model can be effectively utilized while using small images

Sparse Signal Recovery via Tree Search Matching Pursuit

Recently, greedy algorithm has received much attention as a cost-effective means to reconstruct the sparse signals from compressed measurements. Much of previous work has focused on the investigation of a single candidate to identify the support (index set of nonzero elements) of the sparse signals. Well-known drawback of the greedy approach is that the chosen candidate is often not the optimal solution due to the myopic decision in each iteration. In this paper, we propose a tree search based sparse signal recovery algorithm referred to as the tree search matching pursuit (TSMP). Two key ingredients of the proposed TSMP algorithm to control the computational complexity are the pre-selection to put a restriction on columns of the sensing matrix to be investigated and the tree pruning to eliminate unpromising paths from the search tree. In numerical simulations of Internet of Things (IoT) environments, it is shown that TSMP outperforms conventional schemes by a large margin. © 2011 KICS.1

Application of Vibration Signal Processing Methods to Detect and Diagnose Wheel Flats in Railway Vehicles

This paper studied two useful vibration signal processing methods for detection and diagnosis of wheel flats. First, the cepstrum analysis method combined with order analysis was applied to the vibration signal to detect periodic responses in the spectrum for a rotating body such as a wheel. In the case of railway vehicles, changes in speed occur while driving. Thus, it is difficult to effectively evaluate the flat signal of the wheel because the time cycle of the flat signal changes frequently. Thus, the order analysis was combined with the existing cepstrum analysis method to consider the changes in train speed. The order analysis changes the domain of the vibration signal from time domain to rotating angular domain to consider the train speed change in the cepstrum analysis. Second, the cross correlation analysis method combined with the order analysis was applied to evaluate the flat signal from the vibration signal well containing the severe field noise produced by the vibrations of the rail irregularities and bogie components. Unlike the cepstrum analysis method, it can find out the wheel flat size because the flat signal linearly increases to the wheel flat. Thus, it is more effective when checking the size of the wheel flat. Finally, the data tested in the Korea Railroad Research Institute were used to confirm that the cepstrum analysis and cross correlation analysis methods are appropriate for not only simulation but also test data

Application of Vibration Signal Processing Methods to Detect and Diagnose Wheel Flats in Railway Vehicles

This paper studied two useful vibration signal processing methods for detection and diagnosis of wheel flats. First, the cepstrum analysis method combined with order analysis was applied to the vibration signal to detect periodic responses in the spectrum for a rotating body such as a wheel. In the case of railway vehicles, changes in speed occur while driving. Thus, it is difficult to effectively evaluate the flat signal of the wheel because the time cycle of the flat signal changes frequently. Thus, the order analysis was combined with the existing cepstrum analysis method to consider the changes in train speed. The order analysis changes the domain of the vibration signal from time domain to rotating angular domain to consider the train speed change in the cepstrum analysis. Second, the cross correlation analysis method combined with the order analysis was applied to evaluate the flat signal from the vibration signal well containing the severe field noise produced by the vibrations of the rail irregularities and bogie components. Unlike the cepstrum analysis method, it can find out the wheel flat size because the flat signal linearly increases to the wheel flat. Thus, it is more effective when checking the size of the wheel flat. Finally, the data tested in the Korea Railroad Research Institute were used to confirm that the cepstrum analysis and cross correlation analysis methods are appropriate for not only simulation but also test data

Anomaly Detection Method in Railway Using Signal Processing and Deep Learning

In this paper, anomaly detection of wheel flats based on signal processing and deep learning techniques is analyzed. Wheel flats mostly affect running stability and ride comfort. Currently, domestic railway companies visually inspect wheel flats one by one with their eyes after railway vehicles enter the railway depots for maintenance. Therefore, CBM (Condition-Based Maintenance) is required for wheel flats resolution. Anomaly detection for wheel flat signals of railway vehicles using Order analysis and STFT (Short Time Fourier Transform) is studied in this paper. In the case of railway vehicles, it is not easy to obtain actual failure data through running vehicles in a university laboratory due to safety and cost issues. Therefore, vibration-induced acceleration was obtained using a multibody dynamics simulation software, SIMPACK. This method is also proved in the other paper by rig tests. In addition, since the noise signal was not included in the simulated vibration, the noise signal obtained from the Seoul Metro Subway Line 7 vehicle was overlapped with the simulated one. Finally, to improve the performance of both detection rate and real-time of characteristics based on existing LeNet-5 architectures, spectrogram images transformed from time domain data were proceeded with the LeNet deep learning model modified with the pooling method and activation function. As a result, it is validated that the method using the spectrogram with a deep learning approach yields higher accuracy than the time domain data

Use of Contrast Enhancement and High-Resolution 3D Black-Blood MRI to Identify Inflammation in Atherosclerosis

ObjectivesWe investigated the contributing factors for plaque enhancement and examined the relationships between regional contrast enhancement and the inflammatory activity of atherosclerotic plaques in an experimental rabbit model using contrast-enhanced high-resolution 3-dimensional (3D) black-blood magnetic resonance imaging (MRI) in comparison with histopathologic analysis.BackgroundInflammation plays a critical role in plaque initiation, progression, and disruption. As such, inflammation represents an emerging target for the treatment of atherosclerosis. MRI findings suggest that contrast agent–induced signal enhancement is associated with the degree of macrophage infiltration and neovessels that can be detected in plaque.MethodsTen atherosclerotic rabbits and 3 normal control rabbits underwent high-resolution 3D contrast-enhanced black-blood MRI. Magnetic resonance images and the corresponding histopathologic sections were divided into 4 quadrants. Plaque composition was analyzed for each quadrant according to histopathologic criteria (percent of lipid-rich, fibrous, macrophage area and microvessel density) and imaging criteria (enhancement ratio [ER], ER = signal intensitypost/signal intensitypre). Multiple linear regression analysis was performed to determine independent factors for plaque enhancement.ResultsA total of 62 noncalcified plaques (n = 248; 156 lipid-rich quadrants and 92 fibrous quadrants) were identified based on histopathologic analysis. Mean ER values were significantly higher in atherosclerotic vessel walls than in normal vessel walls (2.03 ± 0.25 vs. 1.58 ± 0.15; p = 0.017). The mean ER values were significantly higher in lipid-rich quadrants compared with the fibrous quadrants (2.14 ± 0.31 vs. 1.84 ± 0.21; p = 0.001). Mean ER values were significantly higher in macrophage-rich plaques compared with the macrophage-poor plaques (2.21 ± 0.28 vs. 1.81 ± 0.22; p = 0.001). Using multiple regression analysis, macrophage area and microvessel density were associated independently with ER values that reflected plaque enhancement (p < 0.001).ConclusionsContrast-enhanced high-resolution 3D black-blood MRI may be an efficient method to detect plaque inflammation