Die Wirkung von Peroxisome proliferator-activated receptor-gamma (PPAR-gamma) Agonisten und von körperlicher Aktivität auf das langfristige Schlaganfall-‚outcome’ in einem Mausmodell

Inflammation, as one of the major risk factors for stroke, unifies mechanisms in ischemic stroke pathogenesis, and provides new avenues for stroke prevention— physical exercise, peroxisome proliferator-activated receptor- gamma (PPAR-gamma) agonists, statins, and angiotensin-converting enzyme (ACE) inhibitors. These new stroke prevention therapies may contribute to reduced inflammation, and stabilize the atherosclerotic plaque, or act via other protective mechanisms. Stroke outcome is modulated by the interaction of the injured brain with the immune system. Peroxisome proliferator-activated receptor-gamma (PPAR-gamma) agonists (thiazolidinediones) have anti- inflammatory effects and improve endothelium function. Here, we analyzed the effects of pioglitazone on short- and longer-term outcome after mild transient brain ischemia. 129/SV mice were subjected to 30 min filamentous middle cerebral artery occlusion (MCAo), followed by reperfusion. Post event, animals were treated with daily intraperitoneal (i.p.) pioglitazone (20 mg/kg body weight) or vehicle. Pioglitazone given acutely after transient brain ischemia / reperfusion reduced lesion size and the number of Iba1-expressing microglia in the ischemic striatum at three days. In vitro, pioglitazone attenuated migration and proliferation of primary mouse microglia. However, analysis at 6 weeks after MCAo/reperfusion no longer yielded an effect of pioglitazone on either lesion size or Iba1+ cell counts. Regarding functional longer-term outcome, we also did not detect a beneficial effect of pioglitazone on motor function measured either on the pole test or the wire hanging test or on learning and memory in the Morris water maze. Our study thus underscores the importance of extending experimental stroke studies to an analysis of longer- term outcome. Clinical and experimental evidence indicates that regular physical activity (1) upregulates endothelial nitric oxide synthase (eNOS); (2) improves endothelium-dependent vasodilation; (3) protects from vascular disease in an acute model of ischemic stroke; (4) furthermore, improved neo- vascularization and long-term functional and histological protection through regular physical activity could be shown. Here, we tested the hypothesis that the long-term stroke-protective effects of regular physical activity are mediated via up regulation of eNOS and enhanced neovascularization in a chronic stroke model. To do so, we used N-nitro-L-arginine methyl ester (L-NAME), a pharmacologic inhibitor of NOS and the antiangiogenic agent endostatin. Here, we compared groups of animals subjected to voluntary exercise vs a sedentary lifestyle. After 3 weeks of physical training animals were exposed to mild cerebral ischemia induced by 30 min occlusion of the left middle cerebral artery (MCAo) followed by reperfusion. Then animals were put back to their home cages and treatment was continued as before. A subset of animals from each group was treated either with endostatin or L-NAME respectively. Four weeks after MCAo brain damage in ischemic mice was evaluated by computer-assisted infarct volumetry. We showed abolished neuroprotection after exercise either with co-treatment of L-NAME or endostatin. Our results demonstrate that eNOS upregulation and angiogenesis are implicated in the long-term neuroprotective effects of physical activity.Peroxisome proliferator-activated receptor-gamma (PPAR-gamma)-Agonisten (Thiazolidinedione) haben entzündungshemmende Wirkung und verbessern die Endothelfunktion. Die akute Gabe von Pioglitazon nach transienter Ischämie/Reperfusion reduzierte die Läsionsgröße und die Anzahl der Iba1-exprimierenden Mikrogliazellen im ischämischen Striatum zum Zeitpunkt 3 Tage. In vitro reduzierte Pioglitazon Migration und Proliferation von primären murinen Mikrogliazellen. Eine Analyse 6 Wochen nach MCAo / Reperfusion ergab dagegen keine Effekte einer Pioglitazonbehandlung auf Läsionsgröße, Mikrogliadichte im Ischämieareal oder Verhaltenstests. Die Studie unterstreicht damit die Notwendigkeit, experimentelle Schlaganfallstudien um die Analyse chronischer Endpunkte zu erweitern. Weiterhin wurde hier die langfristige schlaganfallprotektive Wirkung von regelmäßiger körperlicher Aktivität - vermittelt über die Hochregulierung der endothelialen NO Synthase und verstärkte Neovaskularisation - in einem chronischen Schlaganfall-Modell gezeigt. Die positiven Effekte der körperlichen Aktivität konnten dabei sowohl durch Co-Behandlung mit L-NAME oder mit Endostatin blockiert werden. Die Ergebnisse zeigen, dass eNOS- Hochregulation und Angiogenese für die langfristige neuroprotektive Wirkung der körperlichen Aktivität eine wichtige Rolle spielen

Optimization Acceleration Integral Method Based on Power Spectrum Estimation

Due to the excellent performance of the frequency domain integration method, it is widely used for acceleration integral calculations. However, the frequency-domain integration needs to select the effective integration frequency band to achieve its optimal integration performance. This paper proposes the method with power spectrum density (PSD) estimation to realize the optimization integral of the acceleration signal. By analysing the power spectrum density of the acceleration signal, the optimal low frequency cut-off frequency is obtained. Combined with frequency domain integration algorithm, it can effectively remove low-frequency noise and improve integral accuracy. Then, the novel algorithm tested by an experiment platform with a vibration bench. Experiment results show that this algorithm can adaptively select the low-frequency cut-off frequency and realize frequency domain integration optimization and the integration error is controlled within ±0.2mm

An Online Semisupervised Learning Model for Pedestrians’ Crossing Intention Recognition of Connected Autonomous Vehicle Based on Mobile Edge Computing Applications

One of the major challenges that connected autonomous vehicles (CAVs) are facing today is driving in urban environments. To achieve this goal, CAVs need to have the ability to understand the crossing intention of pedestrians. However, for autonomous vehicles, it is quite challenging to understand pedestrians’ crossing intentions. Because the pedestrian is a very complex individual, their intention to cross the street is affected by the weather, the surrounding traffic environment, and even his own emotions. If the established street crossing intention recognition model cannot be updated in real time according to the diversity of samples, the efficiency of human-machine interaction and the interaction safety will be greatly affected. Based on the above problems, this paper established a pedestrian crossing intention model based on the online semisupervised support vector machine algorithm (OS3VM). In order to verify the effectiveness of the model, this paper collects a large amount of pedestrian crossing data and vehicle movement data based on laser scanner, and determines the main feature components of the model input through feature extraction and principal component analysis (PCA). The comparison results of recognition accuracy of SVM, S3VM, and OS3VM indicate that the proposed OS3VM model exhibits a better ability to recognize pedestrian crossing intentions than the SVM and S3VM models, and the accuracy achieves 94.83%. Therefore, the OS3VM model can reduce the number of labeled samples for training the classifier and improve the recognition accuracy

Novel Time-domain Ultra-wide Band TEM Horn Antenna for Highway GPR Applications

Based on transmission line theory and impedance transition, we design an ultra-wideband Transverse ElectroMagnetic (TEM) horn antenna that takes advantage of index gradient structure and loading techniques and is optimized for highway Ground Penetrating Radar (GPR) applications. We use numerical simulation to analyze the effects of different curved surfaces as an extension of the antenna and further improve the antenna performance by the use of a metallic reflective cavity and distributed resistor loading. We then fabricated an antenna based on the optimization results and determined the Voltage Standing Wave Ratio (VSWR) of the antenna to be less than 2 for bandwidths ranging from 0.9–12.6 GHz. The waveform fidelity of the antenna is also good and when we applied this antenna to highway scenarios, it achieved good results

Highly Sensitive Magnetoelastic Biosensor for Alpha2-Macroglobulin Detection Based on MnFe2O4@chitosan/MWCNTs/PDMS Composite

The need for Alpha2-Macroglobulin (α2-M) detection has increased because it plays an important role in the diagnosis of diabetic nephropathy (DN). However, few sensors can realize the high-sensitive detection for α2-M with characteristics of being fast, flexible, wearable and portable. Herein, a biosensor based on a MnFe2O4@chitosan/MWCNTs/PDMS composite film was developed for α2-M detection. Due to the excellent magnetoelastic effect of MnFe2O4 nanoparticles, the stress signal of the biosensor surface induced by the specific antibody–antigen binding was transformed into the electrical and magnetic signal. Chitosan-coated MnFe2O4 particles were used to provide biological modification sites for the α2-M antibody, which simplified the conventional biological functionalization modification process. The MnFe2O4@chitosan particles were successfully prepared by a chemical coprecipitation method and the property was studied by TEM, FT-IR and XRD. MWCNTs were employed to enhance electrical conductivity and the sensitivity of the biosensor. The detection limit (LOD) was reduced to 0.1299 ng·mL−1 in the linear range from 10 ng∙mL−1 to 100 µg·mL−1, which was significantly lower than the limit of health diagnostics. The biosensor is fabricated by a simple method, with advantages of being rapid and highly-sensitive, and having selective detection of α2-M, which provides a novel method for the early diagnosis of DN, and it has potential in the point of care (PoC) field

Deep Learning-Based Automatic Clutter/Interference Detection for HFSWR

High-frequency surface wave radar (HFSWR) plays an important role in wide area monitoring of the marine target and the sea state. However, the detection ability of HFSWR is severely limited by the strong clutter and the interference, which are difficult to be detected due to many factors such as random occurrence and complex distribution characteristics. Hence the automatic detection of the clutter and interference is an important step towards extracting them. In this paper, an automatic clutter and interference detection method based on deep learning is proposed to improve the performance of HFSWR. Conventionally, the Range-Doppler (RD) spectrum image processing method requires the target feature extraction including feature design and preselection, which is not only complicated and time-consuming, but the quality of the designed features is bound up with the performance of the algorithm. By analyzing the features of the target, the clutter and the interference in RD spectrum images, a lightweight deep convolutional learning network is established based on a faster region-based convolutional neural networks (Faster R-CNN). By using effective feature extraction combined with a classifier, the clutter and the interference can be automatically detected. Due to the end-to-end architecture and the numerous convolutional features, the deep learning-based method can avoid the difficulty and absence of uniform standard inherent in handcrafted feature design and preselection. Field experimental results show that the Faster R-CNN based method can automatically detect the clutter and interference with decent performance and classify them with high accuracy

A Flexible Capacitive Paper-Based Pressure Sensor Fabricated Using 3D Printing

Flexible pressure sensors usually exhibit high sensitivity, excellent resolution, and can be mass-produced. Herein, a high-resolution, capacitive, paper-based, 3D-printed pressure sensor with a simple, low-cost preparation method is proposed. The sensor has a wide detection range (300–44,000 Pa), a short response time (<50 ms), and high mechanical stability during repeated loading/unloading (3750 Pa). It can measure the weight of an object precisely, from which the shape of the object can be predicted. The sensor can also perform gait detection. The advantages presented by low-cost, high sensitivity, wide detection range, and the ability to be mass-produced make these sensors potential candidates for applications in contact detection and wearable medical devices

Review of applications and surface smoothing mechanisms of optical waveguide devices

Optical waveguide devices are widely used in many fields and have good development prospects. But surface roughness of waveguide device induces a passive effect on the light transmission loss and the Q value of ring cavity, which restricts the development and applications of optical waveguide devices. Currently, the common used surface and side wall smoothing methods for waveguide devices are the thermal oxidation method, laser beam method, and hydrogen annealing method, and the surface hydrogen annealing method has better smoothing effect. However, the mechanism of hydrogen annealing method is still not clear so far, thus the experimental parameters cannot be further optimized to obtain optimal experimental result. Based on the review of the contents mentioned above, the hydrogen annealing mechanism is primarily studied through the simulation analysis by Materials Studio, which provides theoretical foundation and guidance for smoothing of waveguide device by hydrogen annealing technology