Background: During a migraine attack, trigeminal activation results in the release of calcitonin gene-related peptide (CGRP), which stimulates the release of inflammatory cytokines playing an ...important role in migraine. Objective: We analyze the relation between CGRP and cytokines during attacks to explore the possible mechanism of migraine. Materials and Methods: Migraine patients and healthy control were recruited at the Department of Neurology, the Sixth People's Hospital of Fuyang City, between March 2018 and July 2018. The protein levels of interleukin (IL)-1β, IL-2, IL-6, IL-10, tumor necrosis factor-alpha (TNF-α), and CGRP were determined from the sera of patients with migraine and control subjects by enzyme-linked immunosorbent assay kits. Spearman's rank correlation coefficient was also determined to calculate the correlation between CGRP and inflammatory factors levels. Results: The level of IL-1β, IL-6, TNF-α, and CGRP in migraine group were significantly higher than normal group (P < 0.05). The level of CGRP was significantly correlated with IL-1 β (r = 0.30, P < 0.05) and IL-6 (r = 0.94, P < 0.05), but not significantly correlated with IL-2 (r =−0.047, P = 0.75), IL-10 (r = 0.12, P = 0.43), and TNF-α (r = 0.05, P = 0.72). Conclusions: In our study, we found migraine patients had a higher IL-6, IL-1β, and TNF level than healthy controls and the level of CGRP was related significantly with the level of IL-1β and IL-6. In conclusion, our results suggest that IL-1β and IL-6 may be involved in the pathogenesis of migraine attacks and CGRP related with the secretion of cytokines.
The concerns over land use/land cover (LULC) change have emerged on the global stage due to the realisation that changes occurring on the land surface also influence climate, ecosystem and its ...services. As a result, the importance of accurate mapping of LULC and its changes over time is on the increase. Landsat satellite is a major data source for regional to global LULC analysis. The main objective of this study focuses on the comparison of three classification tools for Landsat images, which are maximum likelihood classification (MLC), support vector machine and artificial neural network (ANN), in order to select the best method among them. The classifiers algorithms are well optimized for the gamma, penalty, degree of polynomial in case of SVM, while for ANN minimum output activation threshold and RMSE are taken into account. The overall analysis shows that the ANN is superior to the kernel based SVM (linear, radial based, sigmoid and polynomial) and MLC. The best tool (ANN) is then applied on detecting the LULC change over part of Walnut Creek, Iowa. The change analysis of the multi temporal images indicates an increase in urban areas and a major shift in the agricultural practices.
Many hydrologic phenomena and applications such as drought, flood, irrigation management and scheduling needs high resolution satellite soil moisture data at a local/regional scale. Downscaling is a ...very important process to convert a coarse domain satellite data to a finer spatial resolution. Three artificial intelligence techniques along with the generalized linear model (GLM) are used to improve the spatial resolution of Soil Moisture and Ocean Salinity (SMOS) derived soil moisture, which is currently available at a very coarse scale of ~40 Km. Artificial neural network (ANN), support vector machine, relevance vector machine and generalized linear models are chosen for this study to integrate the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) with the SMOS derived soil moisture. Soil moisture deficit (SMD) derived from a hydrological model called PDM (Probability Distribution Model) is used for the downscaling performance evaluation. The statistical evaluation has also been made with the day-time and night-time MODIS LST differences with the mean day and night-time PDM SMD data for the selection of effective MODIS products. The accuracy and robustness of all the downscaling algorithms are discussed in terms of their assumptions and applicability. The statistical performance indices such as
R
2
,
%Bias
and
RMSE
indicates that the ANN (
R
2
= 0.751
,
%Bias = −0.628
and
RMSE = 0.011
), RVM (
R
2
= 0.691
,
%Bias = 1.009
and
RMSE = 0.013
), SVM (
R
2
= 0.698
,
%Bias = 2.370
and
RMSE = 0.013
) and GLM (
R
2
= 0.698
,
%Bias = 1.009
and
RMSE = 0.013
) algorithms on the whole are relatively more skillful to downscale the variability of the soil moisture in comparison to the non-downscaled data (
R
2
= 0.418
and
RMSE = 0.017
) with the outperformance of ANN algorithm. The other attempts related to growing and non-growing seasons have been used in this study to reveal that season based downscaling is even better than continuous time series with fairly high performance statistics.
Precipitation, as a primary hydrological variable in the water cycle plays an important role in hydrological modeling. The reliability of hydrological modeling is highly related to the quality of ...precipitation data. Accurate long-term gauged precipitation in the Mekong River Basin, however, is limited. Therefore, the main objective of this study is to assess the performances of various gridded precipitation datasets in rainfall-runoff and flood-inundation modeling of the whole basin. Firstly, the performance of the Rainfall-Runoff-Inundation (RRI) model in this basin was evaluated using the gauged rainfall. The calibration (2000-2003) and validation (2004-2007) results indicated that the RRI model had acceptable performance in the Mekong River Basin. In addition, five gridded precipitation datasets including APHRODITE, GPCC, PERSIANN-CDR, GSMaP (RNL), and TRMM (3B42V7) from 2000 to 2007 were applied as the input to the calibrated model. The results of the simulated river discharge indicated that TRMM, GPCC, and APHRODITE performed better than other datasets. The statistical index of the annual maximum inundated area indicated similar conclusions. Thus, APHRODITE, TRMM, and GPCC precipitation datasets were considered suitable for rainfall-runoff and flood inundation modeling in the Mekong River Basin. This study provides useful guidance for the application of gridded precipitation in hydrological modeling in the Mekong River basin.
Understanding catchment response to rainfall events is important for accurate runoff estimation in many water-related applications, including water resources management. This study introduced a ...hybrid model, the Tank-least squared support vector machine (LSSVM), that incorporated intermediate state variables from a conceptual tank model within the least squared support vector machine (LSSVM) framework in order to describe aspects of the rainfall-runoff (RR) process. The efficacy of the Tank-LSSVM model was demonstrated with hydro-meteorological data measured in the Yongdam Catchment between 2007 and 2016, South Korea. We first explored the role of satellite soil moisture (SM) data (i.e., European Space Agency (ESA) CCI) in the rainfall-runoff modeling. The results indicated that the SM states inferred from the ESA CCISWI provided an effective means of describing the temporal dynamics of SM. Further, the Tank-LSSVM model’s ability to simulate daily runoff was assessed by using goodness of fit measures (i.e., root mean square error, Nash Sutcliffe coefficient (NSE), and coefficient of determination). The Tank-LSSVM models’ NSE were all classified as “very good” based on their performance during the training and testing periods. Compared to individual LSSVM and Tank models, improved daily runoff simulations were seen in the proposed Tank-LSSVM model. In particular, low flow simulations demonstrated the improvement of the Tank-LSSVM model compared to the conventional tank model.
The Low Energy X-ray telescope (LE) is one of the three main instruments of the
Insight-Hard
X-ray Modulation Telescope (
Insight-
HXMT)
.
It is equipped with Swept Charge Device (SCD) sensor arrays ...with a total geometrical area of 384 cm and an energy band from 0.7 to 13 keV. In order to evaluate the particle induced X-ray background and the cosmic X-ray background simultaneously, LE adopts collimators to define four types of Field Of Views (FOVs), i.e., 1.6°×6°, 4°×6°, 50°-60°×2°-6° and the blocked ones which block the X-ray by an aluminum cover. LE is constituted of three detector boxes (LEDs) and an electric control box (LEB) and achieves a good energy resolution of 140 eV@5.9 keV, an excellent time resolution of 0.98 ms, as well as an extremely low pileup (<1%@18000 cts/s). Detailed performance tests and calibration on the ground have been performed, including energy-channel relation, energy response, detection efficiency and time response.
Soil moisture is important in the triggering of many types of landslides. However, in situ soil moisture data are rarely available in hazardous zones. The advanced remote sensing technology could ...provide useful soil moisture information. In this study, an assessment has been carried out between the latest version of the European Space Agency Climate Change Initiative soil moisture product and the landslide events in a northern Italian region in the 14-year period 2002-2015. A clear correlation has been found between the satellite soil moisture and the landslide events, as over four-fifths of events had soil wetness conditions above the 50% regional soil moisture line. Attempts have also been made to explore the soil moisture thresholds for landslide occurrences under different environmental conditions (land cover, soil type, and slope). The results showed slope distribution could provide a rather distinct separation of the soil moisture thresholds, with thresholds becoming smaller for steeper areas, indicating dryer soil condition could trigger landslides at hilly areas than in plain areas. The thresholds validation procedure is then carried out. Forty five rainfall events between 2014 and 2015 are used as test cases. Contingency tables, statistical indicators, and receiver operating characteristic analysis for thresholds under different exceedance probabilities (1%-50%) are explored. The results have shown that the thresholds using 30% exceedance probability provide the best performance with the hitting rate at 0.92 and the false alarm at 0.50. We expect this study can provide useful information for adopting the remotely sensed soil moisture in the landslide early warnings.
This study assesses the usability of Weather Research and Forecasting (WRF) model simulated soil moisture for landslide monitoring in the Emilia Romagna region, northern Italy, during the 10-year ...period between 2006 and 2015. In particular, three advanced land surface model (LSM) schemes (i.e. Noah, Noah-MP, and CLM4) integrated with the WRF are used to provide detailed multi-layer soil moisture information. Through the temporal evaluation with the single-point in situ soil moisture observations, Noah-MP is the only scheme that is able to simulate the large soil drying phenomenon close to the observations during the dry season, and it also has the highest correlation coefficient and the lowest RMSE at most soil layers. It is also demonstrated that a single soil moisture sensor located in a plain area has a high correlation with a significant proportion of the study area (even in the mountainous region 141 km away, based on the WRF-simulated spatial soil moisture information). The evaluation of the WRF rainfall estimation shows there is no distinct difference among the three LSMs, and their performances are in line with a published study for the central USA. Each simulated soil moisture product from the three LSM schemes is then used to build a landslide prediction model, and within each model, 17 different exceedance probability levels from 1 % to 50 % are adopted to determine the optimal threshold scenario (in total there are 612 scenarios). Slope degree information is also used to separate the study region into different groups. The threshold evaluation performance is based on the landslide forecasting accuracy using 45 selected rainfall events between 2014 and 2015. Contingency tables, statistical indicators, and receiver operating characteristic analysis for different threshold scenarios are explored. The results have shown that, for landslide monitoring, Noah-MP at the surface soil layer with 30 % exceedance probability provides the best landslide monitoring performance, with its hit rate at 0.769 and its false alarm rate at 0.289.
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