Far-Infrared Therapy Based on Graphene Ameliorates High-Fat Diet-Induced Anxiety-Like Behavior in Obese Mice via Alleviating Intestinal Barrier Damage and Neuroinflammation.

The consumption of a high-fat diet (HFD) has been implicated in the etiology of obesity and various neuropsychiatric disturbances, including anxiety and depression. Compelling evidence suggests that far-infrared ray (FIR) possesses beneficial effects on emotional disorders. However, the efficacy of FIR therapy in addressing HFD-induced anxiety and the underlying mechanisms remain to be elucidated. Here, we postulate that FIR emitted from a graphene-based therapeutic device may mitigate HFD-induced anxiety behaviors. The graphene-FIR modify the gut microbiota in HFD-mice, particularly by an enriched abundance of beneficial bacteria Clostridiaceae and Erysipelotrichaceae, coupled with a diminution of harmful bacteria Lachnospiraceae, Anaerovoracaceae, Holdemania and Marvinbryantia. Graphene-FIR also improved intestinal barrier function, as evidenced by the augmented expression of the tight junction protein occludin and G protein-coupled receptor 43 (GPR43). In serum level, we observed the decreased free fatty acids (FFA), lipopolysaccharides (LPS), diamine oxidase (DAO) and D-lactate, and increased the glucagon-like peptide-2 (GLP-2) levels in graphene-FIR mice. Simultaneously, inflammatory cytokines IL-6, IL-1ß, and TNF-α manifested a decrease subsequent to graphene-FIR treatment in both peripheral and central system. Notably, graphene-FIR inhibited over expression of astrocytes and microglia. We further noticed that the elevated the BDNF and decreased TLR4 and NF-κB expression in graphene-FIR group. Overall, our study reveals that graphene-FIR rescued HFD-induced anxiety via improving the intestine permeability and the integrity of blood-brain barrier, and reduced inflammatory response by down regulating TLR4/NF-κB inflammatory pathway.

Far-infrared therapy promotes exercise capacity and glucose metabolism in mice by modulating microbiota homeostasis and activating AMPK.

The benefits of physical exercise on human health make it desirable to identify new approaches that would mimic or potentiate the effects of exercise to treat metabolic diseases. However, whether far-infrared (FIR) hyperthermia therapy could be used as exercise mimetic to realize wide-ranging metabolic regulation, and its underling mechanisms remain unclear. Here, a specific far-infrared (FIR) rays generated from graphene-based hyperthermia devices might promote exercise capacity and metabolisms. The material characterization showed that the graphene synthesized by chemical vapour deposition (CVD) was different from carbon fiber, with single-layer structure and high electrothermal transform efficiency. The emission spectra generated by graphene-FIR device would maximize matching those adsorbed by tissues. Graphene-FIR enhanced both core and epidermal temperatures, leading to increased blood flow in the femoral muscle and the abdominal region. The combination of microbiomic and metabolomic analysis revealed that graphene-FIR modulates the metabolism of the gut-muscle axis. This modulation was characterized by an increased abundance of short-chain fatty acids (SCFA)-producing bacteria and AMP, while lactic acid levels decreased. Furthermore, the principal routes involved in glucose metabolism, such as glycolysis and gluconeogenesis, were found to be altered. Graphene-FIR managed to stimulate AMPK activity by activating GPR43, thus enhancing muscle glucose uptake. Furthermore, a microbiota disorder model also demonstrated that the graphene-FIR effectively restore the exercise endurance with enhanced p-AMPK and GLUT4. Our results provided convincing evidence that graphene-based FIR therapy promoted exercise capacity and glucose metabolism via AMPK in gut-muscle axis. These novel findings regarding the therapeutic effects of graphene-FIR suggested its potential utility as a mimetic agent in clinical management of metabolic disorders.

[Division of winter wheat yield estimation by remote sensing based on MODIS EVI time series data and spectral angle clustering].

Crop yield estimation division is the basis of crop yield estimation; it provides an important scientific basis for estimation research and practice. In the paper, MODIS EVI time-series data during winter wheat growth period is selected as the division data; JiangSu province is study area; A division method combined of advanced spectral angle mapping(SVM) and K-means clustering is presented, and tested in winter wheat yield estimation by remote sensing. The results show that: division method of spectral angle clustering can take full advantage of crop growth process that is reflected by MODIS time series data, and can fully reflect region differences of winter wheat that is brought by climate difference. Compared with the traditional division method, yield estimation result based on division result of spectral angle clustering has higher R2 (0.702 6 than 0.624 8) and lower RMSE (343.34 than 381.34 kg x hm(-2)), reflecting the advantages of the new division method in the winter wheat yield estimation. The division method in the paper only use convenient obtaining time-series remote sensing data of low-resolution as division data, can divide winter wheat into similar and well characterized region, accuracy and stability of yield estimation model is also very good, which provides an efficient way for winter wheat estimation by remote sensing, and is conducive to winter wheat yield estimation.

[Winter wheat area estimation with MODIS-NDVI time series based on parcel].

Several attributes of MODIS (moderate resolution imaging spectrometer) data, especially the short temporal intervals and the global coverage, provide an extremely efficient way to map cropland and monitor its seasonal change. However, the reliability of their measurement results is challenged because of the limited spatial resolution. The parcel data has clear geo-location and obvious boundary information of cropland. Also, the spectral differences and the complexity of mixed pixels are weak in parcels. All of these make that area estimation based on parcels presents more advantage than on pixels. In the present study, winter wheat area estimation based on MODIS-NDVI time series has been performed with the support of cultivated land parcel in Tongzhou, Beijing. In order to extract the regional winter wheat acreage, multiple regression methods were used to simulate the stable regression relationship between MODIS-NDVI time series data and TM samples in parcels. Through this way, the consistency of the extraction results from MODIS and TM can stably reach up to 96% when the amount of samples accounts for 15% of the whole area. The results shows that the use of parcel data can effectively improve the error in recognition results in MODIS-NDVI based multi-series data caused by the low spatial resolution. Therefore, with combination of moderate and low resolution data, the winter wheat area estimation became available in large-scale region which lacks completed medium resolution images or has images covered with clouds. Meanwhile, it carried out the preliminary experiments for other crop area estimation.

[Integration of soft and hard classifications using linear spectral mixture model and support vector machines].

This paper presents a new soft and hard classification. By analyzing the target objects in the image distribution, and calculating the adaptive threshold automatically, the image is divided into three regions: pure regions, non-target objects regions and mixed regions. For pure regions and non-target objects regions, hard classification method (support vector machine) is used to quickly extract classified results; For mixed regions, soft classification method (selective endmember for linear spectral mixture model) is used to extract the abundance of target objects. Finally, it generates an integrated soft and hard classification map. In order to evaluate the accuracy of this new method, it is compared with SVM and LSMM using ALOS image. The RMSE value of new method is 0.203, and total accuracy is 95.48%. Both overall accuracies and RMSE show that integration of hard and soft classification has a higher accuracy than single hard or soft classification. Experimental results prove that the new method can effectively solve the problem of mixed pixels, and can obviously improve image classification accuracy.

[Farmland parcel extraction based on high resolution remote sensing image].

A new method of farmland parcel extraction from high resolution remote sensing image based on wavelet and watershed segmentation was proposed in the present paper. First, classification results were used to enhance the contrast of gray-scale value of typical pixels in the original image using the high resolution remote sensing image's spectral information. Second, wavelet transform and watershed segmentation were applied to the enhanced image, then improved region merger algorithm was used to solve the problem of over-segmentation. Finally, inverse wavelet transform was taken to get the reconstructed image, then Canny operator was introduced to add the edge information, and the result of farmland parcel segmentation was obtained. To validate the proposed approach, experiments on Quickbird images were performed, we rapidly extracted the farmland parcel from the test image, and the results had a high accuracy. Despite it had a lot to do in extracting the small size parcels, on the whole the method this paper proposed had a very good robustness. Compared with the traditional methods, it had the following advantages: (1) it was an automatic extraction method, did not need too much manual intervention, and could extract the large area of farmland parcels accurately and effectively. (2) It was a very good solution to the problem of over-segmentation by using improved region merger algorithm, and improved the accuracy of the extraction. All these indicated that the proposed approach was an effective farmland parcel extraction method based on high resolution remote sensing image.

[Spatiotemporal variation patterns of potential evapotranspiration in five provinces of Northwest China in 1960-2011].

By using Penman-Monteith model and Hurst index model, this paper analyzed the spatiotemporal variation patterns of potential evapotranspiration (ET0) in the five provinces of Northwest China in 1960-2011. In the meantime, the dominant factors driving the variations of the ET0 were quantitatively analyzed by using sensitivity analysis method. In 1960-2011, the ET0 in the five provinces presented an overall decreasing trend, with a drop rate of -0.72 mm x a(-1), but the ET0 increased gradually after 1993. An obvious spatial difference was shown in the annual average ET0. The average ET0 in the five provinces was 1158 mm (675-2282 mm), wit the maximum (2282 mm) in Qijiaojing of Xinjiang and the low values (>800 mm) in Qinba Mountains in south Shaanxi. Except in spring, the ET0 in other seasons showed a decreasing trend. In the analysis of future trend, the ET0 in most areas (81.4%) of Northwest China would present a trend from decrease to increase. Therefore, under the background of global warming, the warm and wet degree in Northwest China would be somewhat weakened, but the ET0 in the middle part of Xinjiang would be decreased continuously. Wind speed was the main factor affecting the ET0 in Northwest China at both annual and monthly scales, but the affecting extent of wind speed differed with seasons and areas. The spatial extent affected by the wind speed in winter expanded across the entire five provinces of Northwest China, while the spatial extent affected by the wind speed in summer included the entire Xinjiang and the northwest of Gansu and Qinghai.

[Measurement and dynamic analysis of ecological capital of terrestrial ecosystem in China].

Based on remote sensing and ecological principles, an estimation model of ecological capital was established in this paper. The estimation showed that the ecological capital of terrestrial ecosystem in China was 8.148, 10.86 and 12.44 trillion yuan (RMB) in 1992, 1995 and 2000, respectively. Forest had the highest value (24 673 yuan x hm(-2)), and followed by wetland (21 353 yuan x hm(-2)), both of which gave the most contribution to the ecological capital. According to its spatial distribution, the ecological capital of terrestrial ecosystem in China descended from east to west, and ascended from middle to northeast and south of this country, which was accordant with the vegetation distribution. Owing to the climate change and human activity, the ecological capital of Chinese terrestrial ecosystem ascended from 1992 to 2000, and the spatial distribution of its largest value moved obviously from north to south, with a total change rate of about 20%. However, the ratio between ecological capital and GDP descended significantly in the 1990s.