Study on Static Mechanical Properties and Numerical Simulation of Coral Aggregate Seawater Shotcrete with Reasonable Mix Proportion.

This study aims to explore the static mechanical characteristics of coral aggregate seawater shotcrete (CASS) using an appropriate mix proportion. The orthogonal experiments consisting of four-factor and three-level were conducted to explore an optimal mix proportion of CASS. On a macro-scale, quasi-static compression and splitting tests of CASS with optimal mix proportion at various curing ages employed a combination of acoustic emission (AE) and digital image correlation (DIC) techniques were carried out using an electro-hydraulic servo-controlled test machine. A comparative analysis of static mechanical properties at different curing ages was conducted between the CASS and ordinary aggregate seawater shotcrete (OASS). On a micro-scale, the numerical specimens based on particle flow code (PFC) were subjected to multi-level microcracks division for quantitive analysis of the failure mechanism of specimens. The results show that the optimal mix proportion of CASS consists of 700 kg/m3 of cementitious materials content, a water-binder ratio of 0.45, a sand ratio of 60%, and a dosage of 8% for the accelerator amount. The tensile failure is the primary failure mechanism under uniaxial compression and Brazilian splitting, and the specimens will be closer to the brittle material with increased curing age. The Brazilian splitting failure caused by the arc-shaped main crack initiates from the loading points and propagates along the loading line to the center. Compared with OASS, the CASS has an approximately equal early and low later strength mainly because of the minerals' filling or unfilling effect on coral pores. The rate of increase in CASS is swifter during the initial strength phase and decelerates during the subsequent stages of strength development. The failure in CASS is experienced primarily within the cement mortar and bonding surface between the cement mortar and aggregate.

The association between thromboxane A2 receptor gene polymorphisms and the risk of cerebral infarction.

To explore the association between thromboxane A2 receptor (TXA2R) gene polymorphisms and the risk of cerebral infarction. We screened the relevant publications through the search engines in PubMed, Google Scholar, Embase, Web of Science, and China National Knowledge Infrastructure (the latest search update was performed on July 1, 2020). Gene-disease associations were measured using the estimation of OR (95 % CI) based on five genetic inheritance models. Totally three studies were included in this meta-analysis. TXA2R rs768963 polymorphism in homozygote comparison (OR = 1.86, 95 % CI: 1.35-2.56), heterozygote comparison (OR = 1.81, 95 % CI: 1.37-2.39), and dominant model (OR = 1.82, 95 % CI: 1.39-2.37) emerged as risk factors for cerebral infarction. Besides, an increased cerebral infarction risk was observed in the heterozygote comparison (OR = 1.39, 95 % CI: 1.03-1.88) for TXA2R rs2271875 polymorphism. None of the five models showed any association between TXA2R rs4523 polymorphism and cerebral infarction risk. In conclusion, this is the first meta-analysis verifying that TXA2R rs768963 polymorphism and TXA2R rs2271875 polymorphism may be associated with the risk of cerebral infarction.

Prognostic Value of microRNA-133a Expression and Its Clinicopathologic Significance in Non-Small Cell Lung Cancer: A Comprehensive Study Based on Meta-Analysis and the TCGA Database.

BACKGROUND: The role of microRNA-133a (miR-133a) in non-small cell lung cancers (NSCLCs) is controversial. Thus, we conducted a comprehensive study based on meta-analysis and The Cancer Genome Atlas (TCGA) database. METHODS: Publications were searched in both English and Chinese databases, and meta-analysis was performed using Stata 12.0. The clinical value of miR-133a in NSCLC was investigated by collecting and calculating data from the TCGA database, and the statistical analysis was performed in R 3.5.0. RESULTS: 5 studies with 364 cases were included in this meta-analysis. The combined pooled result showed that high expression of miR-133a was associated with a favorable survival outcome in NSCLC patients (hazard ratio 0.561, 95% confidence interval 0.396-0.794, p = 0.001). Meanwhile, a total of 984 NSCLC patients were extracted from the TCGA database. Results showed an area under the ROC curve value for miR-133a-3p of 0.902, and the expression of miR-133a-3p was linked with clinicopathologic parameters of NSCLC (p < 0.05), including sex, age, social status, and lymph node metastasis. CONCLUSION: Our study indicated that miR-133a might act as a tumor suppressor and be a valuable independent prognostic and diagnostic biomarker for NSCLC, and NSCLC patients with high expression of miR-133 might have a better prognosis.

Ensemble of Rotation Trees for Imbalanced Medical Datasets.

Medical datasets are often predominately composed of "normal" examples with only a small percentage of "abnormal" ones and how to correctly recognize the abnormal examples is very meaningful. However, conventional classification learning methods try to pursue high accuracy by assuming that the number of any class examples is similar to each other, which lead to the fact that the abnormal class examples are usually ignored and misclassified to normal ones. In this paper, we propose a simple but effective ensemble method called ensemble of rotation trees (ERT) to handle this problem in imbalanced medical datasets. ERT learns an ensemble through the following four stages: (1) undersampling subsets from normal class, (2) obtaining new balanced training sets through combining each subset and abnormal class, (3) inducing a rotation matrix on randomly sampling subset of each new balanced set, and in each rotation matrix space, (4) learning a decision tree on each balanced training data. Here, the rotation matrix is mainly to improve the diversity between ensemble members, and undersampling technique aims to improve the performance of learned models on abnormal class. Experimental results show that, compared with other state-of-the-art methods, ERT shows significantly better performance for imbalanced medical datasets.

SCN1A rs3812718 polymorphism is associated with epilepsy: An updated meta-analysis.

To clarify the association between SCN1A rs3812718 polymorphism and epilepsy, we performed an updated meta-analysis. PubMed, Science Direct, Embase, Springer, Google Scholar, and Cochrane databases were searched before January 20, 2018. Odds ratios and 95% confidence intervals were used to assess the strength of associations. Finally, simply eight studies were included in this meta-analysis and all together recruited 7184 individuals, and they consisted of 3595 cases and 3589 controls. Based on the quality evaluation with the NOS, the overall quality of eight studies was scored from seven to eight which indicated good quality. A significant association between SCN1A rs3812718 polymorphism and the risk of epilepsy was detected in the homozygote comparison (OR = 1.64, 95% CI, 1.25-2.15, P = .001, P(BON) = 0.004), and dominant model (OR = 1.36, 95% CI, 1.08-1.72, P < .001, P(BON) < 0.001), but not in heterozygote comparison (OR = 1.22, 95% CI, 0.98-1.53, P = .003, P(BON) = 0.001), and recessive model (OR = 1.35, 95% CI, 1.22-1.49, P = .104, P(BON) = 0.104). In conclusion, our results suggest that SCN1A rs3812718 polymorphism is associated with the risk of epilepsy.

Forest Pruning Based on Branch Importance.

A forest is an ensemble with decision trees as members. This paper proposes a novel strategy to pruning forest to enhance ensemble generalization ability and reduce ensemble size. Unlike conventional ensemble pruning approaches, the proposed method tries to evaluate the importance of branches of trees with respect to the whole ensemble using a novel proposed metric called importance gain. The importance of a branch is designed by considering ensemble accuracy and the diversity of ensemble members, and thus the metric reasonably evaluates how much improvement of the ensemble accuracy can be achieved when a branch is pruned. Our experiments show that the proposed method can significantly reduce ensemble size and improve ensemble accuracy, no matter whether ensembles are constructed by a certain algorithm such as bagging or obtained by an ensemble selection algorithm, no matter whether each decision tree is pruned or unpruned.

Granular computing classification algorithms based on distance measures between granules from the view of set.

Granular computing classification algorithms are proposed based on distance measures between two granules from the view of set. Firstly, granules are represented as the forms of hyperdiamond, hypersphere, hypercube, and hyperbox. Secondly, the distance measure between two granules is defined from the view of set, and the union operator between two granules is formed to obtain the granule set including the granules with different granularity. Thirdly the threshold of granularity determines the union between two granules and is used to form the granular computing classification algorithms based on distance measures (DGrC). The benchmark datasets in UCI Machine Learning Repository are used to verify the performance of DGrC, and experimental results show that DGrC improved the testing accuracies.

Image superresolution reconstruction via granular computing clustering.

The problem of generating a superresolution (SR) image from a single low-resolution (LR) input image is addressed via granular computing clustering in the paper. Firstly, and the training images are regarded as SR image and partitioned into some SR patches, which are resized into LS patches, the training set is composed of the SR patches and the corresponding LR patches. Secondly, the granular computing (GrC) clustering is proposed by the hypersphere representation of granule and the fuzzy inclusion measure compounded by the operation between two granules. Thirdly, the granule set (GS) including hypersphere granules with different granularities is induced by GrC and used to form the relation between the LR image and the SR image by lasso. Experimental results showed that GrC achieved the least root mean square errors between the reconstructed SR image and the original image compared with bicubic interpolation, sparse representation, and NNLasso.

Arsenic contamination and potential health risk implications at an abandoned tungsten mine, southern China.

In an extensive environmental study, field samples, including soil, water, rice, vegetable, fish, human hair and urine, were collected at an abandoned tungsten mine in Shantou City, southern China. Results showed that arsenic (As) concentration in agricultural soils ranged from 3.5 to 935 mg kg(-1) with the mean value of 129 mg kg(-1). In addition, As concentration reached up to 325 microg L(-1) in the groundwater, and the maximum As concentration in local food were 1.09, 2.38 and 0.60 mg kg(-1) for brown rice, vegetable and fish samples, respectively, suggesting the local water resource and food have been severely contaminated with As. Health impact monitoring data revealed that As concentrations in hair and urine samples were up to 2.92 mg kg(-1) and 164 microg L(-1), respectively, indicating a potential health risk among the local residents. Effective measurements should be implemented to protect the local community from the As contamination in the environment.