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The aim of this study was to validate the preventive effects of koumine (KM), a monoterpene indole alkaloid, on gouty arthritis (GA) and to explore its possible mechanisms. C57BL/6 mice were intraperitoneally administered KM (0.8, 2.4 or 7.2 mg/kg), colchicine (3.0 mg/kg) or sterile saline. One hour later, a monosodium urate (MSU) suspension was injected into the right hind paws of the mice to establish an acute gout model. Inflammation symptoms were evaluated at 0, 3, 6, 12 and 24 h, and the mechanical withdrawal threshold was evaluated at 0, 6 and 24 h. After 24 h, the mice were euthanized, and the joint tissue, kidney and blood were collected for subsequent experiments. Histological examination and antioxidant enzyme, kidney index and serum uric acid (UA) measurements were taken. The expression levels of the signalling pathway components were determined. KM effectively alleviated the symptoms of redness, swelling and pain; counteracted inflammatory cell infiltration; and increased antioxidant enzyme levels, reduced kidney index and serum UA levels through regulating UA excretion in MSU‐induced mice. The expression of toll‐like receptor 4 (TLR4)/nuclear factor kappa‐B (NF‐κB)/nucleotide‐binding oligomerization domain, leucine‐rich repeat and pyrin domain‐containing 3 (NLRP3) signalling pathway proteins and mRNA were reduced in the KM group. These results suggest that KM may be effective in alleviating GA through the TLR4/NF‐κB/NLRP3 pathway. [ABSTRACT FROM AUTHOR]

Objective To search age-correlated facial features and construct an age estimation model based on the three-dimensional （3D） facial images of Xinjiang Uygur males, and to structure individual face images of old age and young age. Methods Pretreatment was performed to collect 105 3D facial images of Xingjiang Uygur males aged between 17-57 years by Artec Studio software. The facial images were transferred to high-density 3D dot matrix data by FaceAnalysis software, and each image could be represented with 32 251 vertexes. Central correction of the facial images was done and all the data were aligned to a standard coordinate frame by generalized Procrustes analysis （GPA）. The age estimation model was established by partial least square regression （PLSR）. Furthermore, the changes of age-correlated facial features were presented on the heat map of average face, and the reconstruction of facial images at different ages was performed based on this model. Results With age, the average faces showed a series of changes including the nasolabial sulcus deepening, cheek sinking, cheekbone protruding and eye corner drooping. The Pearson correlation coefficient （r） between estimated age and chronological age was 0.71. The mean absolute deviation （MAD） of age estimation was 6.37 years. The results of age estimation in >30-40 years group showed a best accuracy （MAD=4.27 years）, and the deviations increased with age after 40 years. The composite facial images represented a significant result with age on facial morphological features and aging. Conclusion The results of this study reveal the age-correlated facial features and aging markers in Uygur population, which help to construct a reliable age estimation model.

In this study, idealized cloud-resolving simulations are performed for horizontally uniform and steady southwesterly flow at fixed direction/speed combinations to investigate rainfall characteristics and the role of the complex topography in Taiwan during the Mei-yu season, without the influence of a front or other disturbances. Eight directions (180° to 285°, every 15°) and eight speeds (5 to 22.5 m s-1, every 2.5 m s-1) are considered, and near-surface relative humidity (RH) is also altered (from 55-100 %) in a subset of these tests to further investigate the effects of moisture content, yielding a total 109 experiments each having a integration length of 50 h. Three rainfall regimes that correspond to different ranges of the wet Froude number (Frw) are identified from the idealized simulations (with a grid size of 2 km). The low-Frw regime (Frw ≤ ~0.3) where the island circulation from thermodynamic effects during daytime is the main cause of rainfall in local afternoon. The lower the wind speed (and Frw), the more widespread and amount of rainfall. On the other hand, the high-Frw regime (Frw ≥ ~0.4) occurs when the flow at least 12.5 m s-1 impinges on Taiwan terrain at a large angle to favor the flow-over scenario. Thus, topographic rainfall production becomes dominant through mechanical uplift of unstable air. In this scenario, the faster and wetter the flow, the heavier the rainfall on the windward slopes, with the most favorable direction from 240°-255°. Between the two regimes above, a third and mixed regime also exists. The idealized results are discussed for their applicability to the real atmosphere. [ABSTRACT FROM AUTHOR]

Background: Count time series (e.g., daily deaths) are a very common type of data in environmental health research. The series is generally autocorrelated, while the widely used generalized linear model is based on the assumption of independent outcomes. None of the existing methods for modelling parameter-driven count time series can obtain consistent and reliable standard error of parameter estimates, causing potential inflation of type I error rate. Methods: We proposed a new maximum significant ρ correction (MSRC) method that utilizes information of significant autocorrelation coefficient ρ estimate within 5 orders by moment estimation. A Monte Carlo simulation was conducted to evaluate and compare the finite sample performance of the MSRC and classical unbiased correction (UB-corrected) method. We demonstrated a real-data analysis for assessing the effect of drunk driving regulations on the incidence of road traffic injuries (RTIs) using MSRC in Shenzhen, China. Moreover, there is no previous paper assessing the time-varying intervention effect and considering autocorrelation based on daily data of RTIs. Results: Both methods had a small bias in the regression coefficients. The autocorrelation coefficient estimated by UB-corrected is slightly underestimated at high autocorrelation (≥ 0.6), leading to the inflation of the type I error rate. The new method well controlled the type I error rate when the sample size reached 340. Moreover, the power of MSRC increased with increasing sample size and effect size and decreasing nuisance parameters, and it approached UB-corrected when ρ was small (≤ 0.4), but became more reliable as autocorrelation increased further. The daily data of RTIs exhibited significant autocorrelation after controlling for potential confounding, and therefore the MSRC was preferable to the UB-corrected. The intervention contributed to a decrease in the incidence of RTIs by 8.34% (95% CI, -5.69–20.51%), 45.07% (95% CI, 25.86–59.30%) and 42.94% (95% CI, 9.56–64.00%) at 1, 3 and 5 years after the implementation of the intervention, respectively. Conclusions: The proposed MSRC method provides a reliable and consistent approach for modelling parameter-driven time series with autocorrelated count data. It offers improved estimation compared to existing methods. The strict drunk driving regulations can reduce the risk of RTIs. [ABSTRACT FROM AUTHOR]

A356/SiCP composites were prepared by stir-casting and were foamed immediately using titanium hydride. The compression properties (compressive strength, plateau stress, and absorbed energy) of foams increase with the increase in relative density (RD) irrespective of the SiC content. While an increasing SiC content improves foam properties, the nature of the increase depends on RD. At higher RD, the properties even out up on normalising with respective composite strength. At lower RD, cell wall ruptures, and cracks in lower SiC foams deteriorating their properties. This is observed in Ashby-Gibson model data where, below 0.2 RD, normalised plateau strength is lower than the predictions. The experimental data across the RD and SiC contents can be approximated by a simple power-law relationship. [ABSTRACT FROM AUTHOR]

Recently, an emerging trend in medical image classification is to combine radiomics framework with deep learning classification network in an integrated system. Although this combination is efficient in some tasks, the deep learning-based classification network is often difficult to capture an effective representation of lesion regions, and prone to face the challenge of overfitting, leading to unreliable features and inaccurate results, especially when the sizes of the lesions are small or the training dataset is small. In addition, these combinations mostly lack an effective feature selection mechanism, which makes it difficult to obtain the optimal feature selection. In this paper, we introduce a novel and effective deep semantic segmentation feature-based radiomics (DSFR) framework to overcome the above-mentioned challenges, which consists of two modules: the deep semantic feature extraction module and the feature selection module. Specifically, the extraction module is utilized to extract hierarchical semantic features of the lesions from a trained segmentation network. The feature selection module aims to select the most representative features by using a novel feature similarity adaptation algorithm. Experiments are extensively conducted to evaluate our method in two clinical tasks: the pathological grading prediction in pancreatic neuroendocrine neoplasms (pNENs), and the prediction of thrombolytic therapy efficacy in deep venous thrombosis (DVT). Experimental results on both tasks demonstrate that the proposed method consistently outperforms the state-of-the-art approaches by a large margin. [ABSTRACT FROM AUTHOR]

Agricultural, aquaculture, industrial and domestic activities have placed enormous demands for water, which sometimes results in the over-pumping and associated continually declining groundwater levels. This in turn has led to land subsidence and soil salination. Therefore, it's important to understand the local pumping activities or the pumping rates in order to implement appropriate water management. The distribution of pumping rates varies spatially and temporally due to the availability of surface water and seasonality. In addition, to have correct estimate of the pumping rates, both the hydrology and geology should be consider. SWAT and MODFLOW are employed and run separately to acquire certain hydrologic components such as the recharge, boundary flow and change of aquifer storage in multi-aquifers. The water balance method (WBM) is then adopted to estimate pumping rates with these components. To validate the proposed model the results of WBM and the official records are compared. Besides, in view of the serious land subsidence occurred, artificial recharge is regarded as an effective tool to alleviate and mitigate the subsidence. Nevertheless, the location of conducting artificial recharge needs to be identified first. The potential recharge zones are assessed based on the simulated recharge rates from SWAT and the spatial distribution of hydrological characteristics of the unconfined aquifer. Ultimately, an optimal recharge zone will be suggested. The proposed methodology is proved capable of estimating the pumping rates and locating the potential recharge zone. [ABSTRACT FROM AUTHOR]