Chk2-dependent HuR phosphorylation regulates occludin mRNA translation and epithelial barrier function

Occludin is a transmembrane tight junction (TJ) protein that plays an important role in TJ assembly and regulation of the epithelial barrier function, but the mechanisms underlying its post-transcriptional regulation are unknown. The RNA-binding protein HuR modulates the stability and translation of many target mRNAs. Here, we investigated the role of HuR in the regulation of occludin expression and therefore in the intestinal epithelial barrier function. HuR bound the 3′-untranslated region of the occludin mRNA and enhanced occludin translation. HuR association with the occludin mRNA depended on Chk2-dependent HuR phosphorylation. Reduced HuR phosphorylation by Chk2 silencing or by reduction of Chk2 through polyamine depletion decreased HuR-binding to the occludin mRNA and repressed occludin translation, whereas Chk2 overexpression enhanced (HuR/occludin mRNA) association and stimulated occludin expression. In mice exposed to septic stress induced by cecal ligation and puncture, Chk2 levels in the intestinal mucosa decreased, associated with an inhibition of occludin expression and gut barrier dysfunction. These results indicate that HuR regulates occludin mRNA translation through Chk2-dependent HuR phosphorylation and that this influence is crucial for maintenance of the epithelial barrier integrity in the intestinal tract

A Method for Detecting Outliers from the Gamma Distribution

Outliers often occur during data collection, which could impact the result seriously and lead to a large inference error; therefore, it is important to detect outliers before data analysis. Gamma distribution is a popular distribution in statistics; this paper proposes a method for detecting multiple upper outliers from gamma (m,θ). For computing the critical value of the test statistic in our method, we derive the density function for the case of a single outlier and design two algorithms based on the Monte Carlo and the kernel density estimation for the case of multiple upper outliers. A simulation study shows that the test statistic proposed in this paper outperforms some common test statistics. Finally, we propose an improved testing method to reduce the impact of the swamping effect, which is demonstrated by real data analyses

Outlier detection method for geotechnical engineering based on MetaOD model selection

The geotechnical engineering field and indoor parameter test data are the foundation of engineering construction, design and evaluation. The existence of abnormal data often misleads the determination of parameters such as construction and design. Data anomaly detection is the most basic but extremely important task to ensure the safety and reliability of a project. Aiming at the blindness of detection due to the lack of model selection in traditional anomaly detection algorithms, this paper proposes an anomaly detection model system based on a combination of meta-learning outlier detection (MetaOD) and data mining algorithms. The system first selects the initial model class and its parameters suitable for different data types according to the characteristics of the data, averages the selected parameters of the same type of algorithm, and then uses the selected algorithm to diagnose data anomalies, thereby improving the anomaly accuracy of detection. To evaluate the effectiveness of the model, the machine learning test dataset (glass dataset) proposed by the University of California Irvine, is used for test analysis. The results show that the accuracy rate of anomaly detection using this model system reaches 96.41%, which is much higher than that of other detection algorithms. Finally, the model system is applied to the uniaxial compressive strength dataset of the Macau granite and the groundwater monitoring data of the Junchang Tunnel to carry out anomaly detection and analysis and to identify 9 and 10 abnormal points, respectively

Multifocal Electroretinogram Can Detect the Abnormal Retinal Change in Early Stage of type2 DM Patients without Apparent Diabetic Retinopathy

Purpose. To study retinal function defects in type 2 diabetic patients without clinically apparent retinopathy using a multifocal electroretinogram (mf-ERG). Methods. Seventy-six eyes of thirty-eight type 2 diabetes mellitus(DM) patients without clinically apparent retinopathy and sixty-four normal eyes of thirty-two healthy control (HC) participants were examined using mf-ERG. Results. Patients with type 2 DM without apparent diabetic retinopathy demonstrated an obvious implicit time delay of P1 in ring 1, ring 3, and ring 5 compared with healthy controls (t=5.184, p≤0.001; t=8.077, p≤0.001; t=2.000, p=0.047, respectively). The implicit time (IT) in ring 4 of N1wave was significantly delayed in the DM group (t=2.327, p=0.021). Compared with the HC group, the implicit time of the P1 and N1 waves in the temporal retina zone was significantly prolonged (t=3.66, p≤0.001; t=2.187, p=0.03, respectively). And the amplitude of P1 in the temporal retina decreased in the DM group, which had a significantly statistical difference with the healthy controls (t=−6.963, p≤0.001). However, there were no differences in either the amplitude of the response or the implicit time of the nasal retina zone between DM and HC. The AUC of multiparameters of mf-ERG was higher in the diagnosis of DR patients. Conclusions. Patients with type 2 DM without clinically apparent retinopathy had a delayed implicit time of P1 wave in temporal regions of the postpole of the retina compared with HC subjects. It demonstrates that mf-ERG can detect the abnormal retinal change in the early stage of type2 DM patients without apparent diabetic retinopathy. Multiparameters of mf-ERG can improve the diagnostic efficacy of DR, and it may be a potential clinical biomarker for early diagnosis of DR

An Ultrasensitive Picric Acid Sensor Based on a Robust 3D Hydrogen-Bonded Organic Framework

Hydrogen-bonded organic frameworks (HOFs), as a newly developed porous material, have been widely used in various fields. To date, several organic building units (OBUs) with tri-, tetra-, and hexa-carboxylic acid synthons have been applied to synthesize HOFs. To our knowledge, di-carboxylic acids have rarely been reported for the construction of HOFs, in particular, di-carboxylic acid-based HOFs with fluorescence sensing properties have not been reported. In this study, a rare example of a di-carboxylic acid-based, luminescent three-dimensional hydrogen-bonded organic framework has been successfully constructed and structurally characterized; it has a strong electron-rich property originated from its organic linker 9-phenylcarbazole-3,6-dicarboxylic acid. It represents the first example of HOF-based sensors for the highly selective and sensitive detection of PA (Picric acid) with reusability; the LOD is less than 60 nM. This work thus provides a new avenue for the fabrication of fluorescent HOFs sensing towards explosives

A Rigorously-Incremental Spatiotemporal Data Fusion Method for Fusing Remote Sensing Images

The spatiotemporal remote sensing images have significant importance in forest ecological monitoring, forest carbon management, and other related fields. Spatiotemporal data fusion technology of remote sensing images combines high spatiotemporal and high temporal resolution images to address the current limitation of single sensors in obtaining high spatiotemporal resolution. This technology has gained widespread attention in recent years. However, the current models still exhibit some shortcomings in dealing with land cover changes, such as poor clustering results, inaccurate incremental spatiotemporal calculations, and sensor differences. In this article, we propose a rigorously-incremental spatiotemporal data fusion method for fusing remote sensing images with different resolutions to address the aforementioned problems. The proposed method utilizes the particle swarm optimization Gaussian mixture model to extract endmembers and establishes a linear relationship between sensors to obtain accurate time increments. Furthermore, bicubic interpolation is used instead of thin plate spline interpolation for spatial interpolation, and also support vector regression is used to calculate weights for obtaining a weighted sum of temporal and spatial increments. In addition, sensor errors are allocated to the calculation of residuals. The experimental results show the efficacy of the proposed algorithm for fusing fine image Landsat with coarse image MODIS data and conclude that the proposed algorithm presents a better solution for heterogeneous data with strong phenological changes and regions with changes in surface types, which provides a better solution for remote sensing image fusion and, hence, improves the accuracy, stability, and robustness of data fusion