Anti-inflammatory role of fenofibrate in treating diseases

Inflammation contributes to the pathogenesis of several diseases. Fenofibrate, known as a peroxisome proliferator-activated receptor - α (PPAR-α) agonist, is a classic drug for treating hyperlipidemia. In addition to its lipid-lowering effect, fenofibrate has also been reported to exert anti-inflammatory effects with complicated underlying mechanisms of action. In general, the anti-inflammatory effect of fenofibrate is secondary to its lipid-lowering effect, especially for the inflammation caused by hyperlipidemia in the circulatory system. Some anti-inflammatory actions may also come from its regulatory effects on intracellular lipid metabolism by activating PPAR-α. In addition, some roles in anti-inflammation might be mediated by its direct regulation of inflammatory signaling pathways. In order to understand anti-inflammatory activities and the underlying mechanisms of fenofibrate action in disease better, we herein reviewed and discussed the anti-inflammatory roles and its subserving mechanisms in various diseases of different organ systems. Thus, this review offers insights into the optimal use of fenofibrate in the clinical setting

Isomeric Effects of Solution Processed Ladderâ Type Nonâ Fullerene Electron Acceptors

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138922/1/solr201700107_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138922/2/solr201700107-sup-0001-SuppData-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138922/3/solr201700107.pd

Near-infrared photoactivatable control of Ca2+ signaling and optogenetic immunomodulation

The application of current channelrhodopsin-based optogenetic tools is limited by the lack of strict ion selectivity and the inability to extend the spectra sensitivity into the near-infrared (NIR) tissue transmissible range. Here we present an NIR-stimulable optogenetic platform (termed 'Opto-CRAC') that selectively and remotely controls Ca(2+) oscillations and Ca(2+)-responsive gene expression to regulate the function of non-excitable cells, including T lymphocytes, macrophages and dendritic cells. When coupled to upconversion nanoparticles, the optogenetic operation window is shifted from the visible range to NIR wavelengths to enable wireless photoactivation of Ca(2+)-dependent signaling and optogenetic modulation of immunoinflammatory responses. In a mouse model of melanoma by using ovalbumin as surrogate tumor antigen, Opto-CRAC has been shown to act as a genetically-encoded 'photoactivatable adjuvant' to improve antigen-specific immune responses to specifically destruct tumor cells. Our study represents a solid step forward towards the goal of achieving remote and wireless control of Ca(2+)-modulated activities with tailored function. DOI: http://dx.doi.org/10.7554/eLife.10024.00

EFFECT OF REACTION CONDITIONS ON THE SYNTHESIS OF ULTRAFINE AlN POWDER WITH GLUCOSE AS CARBON SOURCE

Using aluminum hydroxide (Al(OH)₃) and aluminum nitrate (Al(NO₃)₃.9H₂O) as aluminum sources and highly active glucose (C₆H12O₆) as a carbon source, we synthesized ultrafine AlN powder through carbothermal reduction-nitridation. Then, we explored the effects of aluminum sources, reaction temperature, holding time and other reaction conditions on the phase composition and micromorphology of the synthesized AlN powder. Result showed that AlN powder and different proportions of Al₂O₃-AlN composite powder can be prepared by controlling reaction temperature or holding time. Under the conditions of this experiment, Al(OH)₃ more beneficial as an aluminum source for carbothermal reduction-nitridation than Al(NO₃)₃.9H₂O. The optimum reaction conditions for the synthesis of single-phase AlN powder with Al(OH)₃ as aluminum source were holding time of 3 h and temperature of 1450 °C. The powder sample synthesized under this reaction condition is mainly composed of flaky, short-rod, and irregularly shaped fine particles with sizes ranging from 50 nm to 100 nm

mmSafe: A Voice Security Verification System Based on Millimeter-Wave Radar

With the increasing popularity of smart devices, users can control their mobile phones, TVs, cars, and smart furniture by using voice assistants, but voice assistants are susceptible to intrusion by outsider speakers or playback attacks. In order to address this security issue, a millimeter-wave radar-based voice security authentication system is proposed in this paper. First, the speaker’s fine-grained vocal cord vibration signal is extracted by eliminating static object clutter and motion effects; second, the weighted Mel Frequency Cepstrum Coefficients (MFCCs) are obtained as biometric features; and finally, text-independent security authentication is performed by the WMHS (Weighted MFCCs and Hog-based SVM) method. This system is highly adaptable and can authenticate designated speakers, resist intrusion by other unspecified speakers as well as playback attacks, and is secure for smart devices. Extensive experiments have verified that the system achieves a 93.4% speaker verification accuracy and a 5.8% miss detection rate for playback attacks

mmSight: A Robust Millimeter-Wave Near-Field SAR Imaging Algorithm

Millimeter-wave SAR (Synthetic Aperture Radar) imaging is widely studied as a common means of RF (Radio Frequency) imaging, but there are problems of the ghost image in Sparsely-Sampled cases and the projection of multiple targets at different distances. Therefore, a robust imaging algorithm based on the Analytic Fourier Transform is proposed, which is named mmSight. First, the original data are windowed with Blackman window to take multiple distance planes into account; then, the Analytic Fourier Transform that can effectively suppress the ghost image under Sparsely-Sampled is used for imaging; finally, the results are filtered using a Mean Filter to remove spatial noise. The experimental results show that the proposed imaging algorithm in this paper, relative to other algorithms, can image common Fully-Sampled single target, hidden target, and multiple targets at the same distance, and solve the ghost image problem of single target in the case of Sparsely-Sampled, as well as the projection problem of multiple targets at different distances; the Image Entropy of the mmSight is 4.6157 and is on average 0.3372 lower than that of other algorithms. Compared with other algorithms, the sidelobe and noise of the Point Spread Function are suppressed, so the quality of the image obtained from imaging is better than that of other algorithms

Millimeter wave gesture recognition using multi-feature fusion models in complex scenes

Abstract As a form of body language, the gesture plays an important role in smart homes, game interactions, and sign language communication, etc. The gesture recognition methods have been carried out extensively. The existing methods have inherent limitations regarding user experience, visual environment, and recognition granularity. Millimeter wave radar provides an effective method for the problems lie ahead gesture recognition because of the advantage of considerable bandwidth and high precision perception. Interfering factors and the complexity of the model raise an enormous challenge to the practical application of gesture recognition methods as the millimeter wave radar is applied to complex scenes. Based on multi-feature fusion, a gesture recognition method for complex scenes is proposed in this work. We collected data in variety places to improve sample reliability, filtered clutters to improve the signal’s signal-to-noise ratio (SNR), and then obtained multi features involves range-time map (RTM), Doppler-time map (DTM) and angle-time map (ATM) and fused them to enhance the richness and expression ability of the features. A lightweight neural network model multi-CNN-LSTM is designed to gestures recognition. This model consists of three convolutional neural network (CNN) for three obtained features and one long short-term memory (LSTM) for temporal features. We analyzed the performance and complexity of the model and verified the effectiveness of feature extraction. Numerous experiments have shown that this method has generalization ability, adaptability, and high robustness in complex scenarios. The recognition accuracy of 14 experimental gestures reached 97.28%

mmSight: A Robust Millimeter-Wave Near-Field SAR Imaging Algorithm

Millimeter-wave SAR (Synthetic Aperture Radar) imaging is widely studied as a common means of RF (Radio Frequency) imaging, but there are problems of the ghost image in Sparsely-Sampled cases and the projection of multiple targets at different distances. Therefore, a robust imaging algorithm based on the Analytic Fourier Transform is proposed, which is named mmSight. First, the original data are windowed with Blackman window to take multiple distance planes into account; then, the Analytic Fourier Transform that can effectively suppress the ghost image under Sparsely-Sampled is used for imaging; finally, the results are filtered using a Mean Filter to remove spatial noise. The experimental results show that the proposed imaging algorithm in this paper, relative to other algorithms, can image common Fully-Sampled single target, hidden target, and multiple targets at the same distance, and solve the ghost image problem of single target in the case of Sparsely-Sampled, as well as the projection problem of multiple targets at different distances; the Image Entropy of the mmSight is 4.6157 and is on average 0.3372 lower than that of other algorithms. Compared with other algorithms, the sidelobe and noise of the Point Spread Function are suppressed, so the quality of the image obtained from imaging is better than that of other algorithms